def main(args=None):
if not args:
args = sys.argv[1:]
# initialize parameters
param = Param()
# call main function
parser = get_parser()
arguments = parser.parse(args)
fname_data = arguments['-i']
fname_bvecs = arguments['-bvec']
average = arguments['-a']
verbose = int(arguments['-v'])
remove_temp_files = int(arguments['-r'])
path_out = arguments['-ofolder']
if '-bval' in arguments:
fname_bvals = arguments['-bval']
else:
fname_bvals = ''
if '-bvalmin' in arguments:
param.bval_min = arguments['-bvalmin']
# Initialization
start_time = time.time()
# sct.printv(arguments)
sct.printv('\nInput parameters:', verbose)
sct.printv(' input file ............' + fname_data, verbose)
sct.printv(' bvecs file ............' + fname_bvecs, verbose)
sct.printv(' bvals file ............' + fname_bvals, verbose)
sct.printv(' average ...............' + str(average), verbose)
# Get full path
fname_data = os.path.abspath(fname_data)
fname_bvecs = os.path.abspath(fname_bvecs)
if fname_bvals:
fname_bvals = os.path.abspath(fname_bvals)
# Extract path, file and extension
path_data, file_data, ext_data = sct.extract_fname(fname_data)
# # get output folder
# if path_out == '':
# path_out = ''
# create temporary folder
path_tmp = sct.tmp_create(basename="dmri_separate", verbose=verbose)
# copy files into tmp folder and convert to nifti
sct.printv('\nCopy files into temporary folder...', verbose)
ext = '.nii'
dmri_name = 'dmri'
b0_name = 'b0'
b0_mean_name = b0_name + '_mean'
dwi_name = 'dwi'
dwi_mean_name = dwi_name + '_mean'
from sct_convert import convert
if not convert(fname_data, os.path.join(path_tmp, dmri_name + ext)):
sct.printv('ERROR in convert.', 1, 'error')
sct.copy(fname_bvecs, os.path.join(path_tmp, "bvecs"), verbose=verbose)
# go to tmp folder
curdir = os.getcwd()
os.chdir(path_tmp)
# Get size of data
im_dmri = Image(dmri_name + ext)
sct.printv('\nGet dimensions data...', verbose)
nx, ny, nz, nt, px, py, pz, pt = im_dmri.dim
sct.printv(
'.. ' + str(nx) + ' x ' + str(ny) + ' x ' + str(nz) + ' x ' + str(nt),
verbose)
# Identify b=0 and DWI images
sct.printv(fname_bvals)
index_b0, index_dwi, nb_b0, nb_dwi = identify_b0(fname_bvecs, fname_bvals,
param.bval_min, verbose)
# Split into T dimension
sct.printv('\nSplit along T dimension...', verbose)
im_dmri_split_list = split_data(im_dmri, 3)
for im_d in im_dmri_split_list:
im_d.save()
# Merge b=0 images
sct.printv('\nMerge b=0...', verbose)
from sct_image import concat_data
l = []
for it in range(nb_b0):
l.append(dmri_name + '_T' + str(index_b0[it]).zfill(4) + ext)
im_out = concat_data(l, 3).save(b0_name + ext)
# Average b=0 images
if average:
sct.printv('\nAverage b=0...', verbose)
sct.run([
'sct_maths', '-i', b0_name + ext, '-o', b0_mean_name + ext,
'-mean', 't'
], verbose)
# Merge DWI
l = []
for it in range(nb_dwi):
l.append(dmri_name + '_T' + str(index_dwi[it]).zfill(4) + ext)
im_out = concat_data(l, 3).save(dwi_name + ext)
# Average DWI images
if average:
sct.printv('\nAverage DWI...', verbose)
sct.run([
'sct_maths', '-i', dwi_name + ext, '-o', dwi_mean_name + ext,
'-mean', 't'
], verbose)
# if not average_data_across_dimension('dwi.nii', 'dwi_mean.nii', 3):
# sct.printv('ERROR in average_data_across_dimension', 1, 'error')
# sct.run(fsloutput + 'fslmaths dwi -Tmean dwi_mean', verbose)
# come back
os.chdir(curdir)
# Generate output files
sct.printv('\nGenerate output files...', verbose)
sct.generate_output_file(os.path.join(path_tmp, b0_name + ext),
os.path.join(path_out, b0_name + ext_data),
verbose)
sct.generate_output_file(os.path.join(path_tmp, dwi_name + ext),
os.path.join(path_out, dwi_name + ext_data),
verbose)
if average:
sct.generate_output_file(
os.path.join(path_tmp, b0_mean_name + ext),
os.path.join(path_out, b0_mean_name + ext_data), verbose)
sct.generate_output_file(
os.path.join(path_tmp, dwi_mean_name + ext),
os.path.join(path_out, dwi_mean_name + ext_data), verbose)
# Remove temporary files
if remove_temp_files == 1:
sct.printv('\nRemove temporary files...', verbose)
sct.rmtree(path_tmp, verbose=verbose)
# display elapsed time
elapsed_time = time.time() - start_time
sct.printv(
'\nFinished! Elapsed time: ' + str(int(np.round(elapsed_time))) + 's',
verbose)
# to view results
sct.printv('\nTo view results, type: ', verbose)
if average:
sct.display_viewer_syntax(['b0', 'b0_mean', 'dwi', 'dwi_mean'])
else:
sct.display_viewer_syntax(['b0', 'dwi'])
def moco(param):
"""
Main function that performs motion correction.
:param param:
:return:
"""
# retrieve parameters
file_data = param.file_data
file_target = param.file_target
folder_mat = param.mat_moco # output folder of mat file
todo = param.todo
suffix = param.suffix
verbose = param.verbose
# other parameters
file_mask = 'mask.nii'
printv('\nInput parameters:', param.verbose)
printv(' Input file ............ ' + file_data, param.verbose)
printv(' Reference file ........ ' + file_target, param.verbose)
printv(' Polynomial degree ..... ' + param.poly, param.verbose)
printv(' Smoothing kernel ...... ' + param.smooth, param.verbose)
printv(' Gradient step ......... ' + param.gradStep, param.verbose)
printv(' Metric ................ ' + param.metric, param.verbose)
printv(' Sampling .............. ' + param.sampling, param.verbose)
printv(' Todo .................. ' + todo, param.verbose)
printv(' Mask ................. ' + param.fname_mask, param.verbose)
printv(' Output mat folder ..... ' + folder_mat, param.verbose)
try:
os.makedirs(folder_mat)
except FileExistsError:
pass
# Get size of data
printv('\nData dimensions:', verbose)
im_data = Image(param.file_data)
nx, ny, nz, nt, px, py, pz, pt = im_data.dim
printv(
(' ' + str(nx) + ' x ' + str(ny) + ' x ' + str(nz) + ' x ' + str(nt)),
verbose)
# copy file_target to a temporary file
printv('\nCopy file_target to a temporary file...', verbose)
file_target = "target.nii.gz"
convert(param.file_target, file_target, verbose=0)
# Check if user specified a mask
if not param.fname_mask == '':
# Check if this mask is soft (i.e., non-binary, such as a Gaussian mask)
im_mask = Image(param.fname_mask)
if not np.array_equal(im_mask.data, im_mask.data.astype(bool)):
# If it is a soft mask, multiply the target by the soft mask.
im = Image(file_target)
im_masked = im.copy()
im_masked.data = im.data * im_mask.data
im_masked.save(
verbose=0) # silence warning about file overwritting
# If scan is sagittal, split src and target along Z (slice)
if param.is_sagittal:
dim_sag = 2 # TODO: find it
# z-split data (time series)
im_z_list = split_data(im_data, dim=dim_sag, squeeze_data=False)
file_data_splitZ = []
for im_z in im_z_list:
im_z.save(verbose=0)
file_data_splitZ.append(im_z.absolutepath)
# z-split target
im_targetz_list = split_data(Image(file_target),
dim=dim_sag,
squeeze_data=False)
file_target_splitZ = []
for im_targetz in im_targetz_list:
im_targetz.save(verbose=0)
file_target_splitZ.append(im_targetz.absolutepath)
# z-split mask (if exists)
if not param.fname_mask == '':
im_maskz_list = split_data(Image(file_mask),
dim=dim_sag,
squeeze_data=False)
file_mask_splitZ = []
for im_maskz in im_maskz_list:
im_maskz.save(verbose=0)
file_mask_splitZ.append(im_maskz.absolutepath)
# initialize file list for output matrices
file_mat = np.empty((nz, nt), dtype=object)
# axial orientation
else:
file_data_splitZ = [file_data] # TODO: make it absolute like above
file_target_splitZ = [file_target] # TODO: make it absolute like above
# initialize file list for output matrices
file_mat = np.empty((1, nt), dtype=object)
# deal with mask
if not param.fname_mask == '':
convert(param.fname_mask, file_mask, squeeze_data=False, verbose=0)
im_maskz_list = [Image(file_mask)
] # use a list with single element
# Loop across file list, where each file is either a 2D volume (if sagittal) or a 3D volume (otherwise)
# file_mat = tuple([[[] for i in range(nt)] for i in range(nz)])
file_data_splitZ_moco = []
printv(
'\nRegister. Loop across Z (note: there is only one Z if orientation is axial)'
)
for file in file_data_splitZ:
iz = file_data_splitZ.index(file)
# Split data along T dimension
# printv('\nSplit data along T dimension.', verbose)
im_z = Image(file)
list_im_zt = split_data(im_z, dim=3)
file_data_splitZ_splitT = []
for im_zt in list_im_zt:
im_zt.save(verbose=0)
file_data_splitZ_splitT.append(im_zt.absolutepath)
# file_data_splitT = file_data + '_T'
# Motion correction: initialization
index = np.arange(nt)
file_data_splitT_num = []
file_data_splitZ_splitT_moco = []
failed_transfo = [0 for i in range(nt)]
# Motion correction: Loop across T
for indice_index in sct_progress_bar(range(nt),
unit='iter',
unit_scale=False,
desc="Z=" + str(iz) + "/" +
str(len(file_data_splitZ) - 1),
ascii=False,
ncols=80):
# create indices and display stuff
it = index[indice_index]
file_mat[iz][it] = os.path.join(
folder_mat,
"mat.Z") + str(iz).zfill(4) + 'T' + str(it).zfill(4)
file_data_splitZ_splitT_moco.append(
add_suffix(file_data_splitZ_splitT[it], '_moco'))
# deal with masking (except in the 'apply' case, where masking is irrelevant)
input_mask = None
if not param.fname_mask == '' and not param.todo == 'apply':
# Check if mask is binary
if np.array_equal(im_maskz_list[iz].data,
im_maskz_list[iz].data.astype(bool)):
# If it is, pass this mask into register() to be used
input_mask = im_maskz_list[iz]
else:
# If not, do not pass this mask into register() because ANTs cannot handle non-binary masks.
# Instead, multiply the input data by the Gaussian mask.
im = Image(file_data_splitZ_splitT[it])
im_masked = im.copy()
im_masked.data = im.data * im_maskz_list[iz].data
im_masked.save(
verbose=0) # silence warning about file overwritting
# run 3D registration
failed_transfo[it] = register(param,
file_data_splitZ_splitT[it],
file_target_splitZ[iz],
file_mat[iz][it],
file_data_splitZ_splitT_moco[it],
im_mask=input_mask)
# average registered volume with target image
# N.B. use weighted averaging: (target * nb_it + moco) / (nb_it + 1)
if param.iterAvg and indice_index < 10 and failed_transfo[
it] == 0 and not param.todo == 'apply':
im_targetz = Image(file_target_splitZ[iz])
data_targetz = im_targetz.data
data_mocoz = Image(file_data_splitZ_splitT_moco[it]).data
data_targetz = (data_targetz * (indice_index + 1) +
data_mocoz) / (indice_index + 2)
im_targetz.data = data_targetz
im_targetz.save(verbose=0)
# Replace failed transformation with the closest good one
fT = [i for i, j in enumerate(failed_transfo) if j == 1]
gT = [i for i, j in enumerate(failed_transfo) if j == 0]
for it in range(len(fT)):
abs_dist = [np.abs(gT[i] - fT[it]) for i in range(len(gT))]
if not abs_dist == []:
index_good = abs_dist.index(min(abs_dist))
printv(
' transfo #' + str(fT[it]) + ' --> use transfo #' +
str(gT[index_good]), verbose)
# copy transformation
copy(file_mat[iz][gT[index_good]] + 'Warp.nii.gz',
file_mat[iz][fT[it]] + 'Warp.nii.gz')
# apply transformation
sct_apply_transfo.main(args=[
'-i', file_data_splitZ_splitT[fT[it]], '-d', file_target,
'-w', file_mat[iz][fT[it]] + 'Warp.nii.gz', '-o',
file_data_splitZ_splitT_moco[fT[it]], '-x', param.interp
])
else:
# exit program if no transformation exists.
printv(
'\nERROR in ' + os.path.basename(__file__) +
': No good transformation exist. Exit program.\n', verbose,
'error')
sys.exit(2)
# Merge data along T
file_data_splitZ_moco.append(add_suffix(file, suffix))
if todo != 'estimate':
im_out = concat_data(file_data_splitZ_splitT_moco, 3)
im_out.absolutepath = file_data_splitZ_moco[iz]
im_out.save(verbose=0)
# If sagittal, merge along Z
if param.is_sagittal:
# TODO: im_out.dim is incorrect: Z value is one
im_out = concat_data(file_data_splitZ_moco, 2)
dirname, basename, ext = extract_fname(file_data)
path_out = os.path.join(dirname, basename + suffix + ext)
im_out.absolutepath = path_out
im_out.save(verbose=0)
return file_mat, im_out
def fmri_moco(param):
file_data = 'fmri'
ext_data = '.nii'
mat_final = 'mat_final/'
fsloutput = 'export FSLOUTPUTTYPE=NIFTI; ' # for faster processing, all outputs are in NIFTI
ext_mat = 'Warp.nii.gz' # warping field
# Get dimensions of data
sct.printv('\nGet dimensions of data...', param.verbose)
nx, ny, nz, nt, px, py, pz, pt = Image(file_data + '.nii').dim
sct.printv(
' ' + str(nx) + ' x ' + str(ny) + ' x ' + str(nz) + ' x ' + str(nt),
param.verbose)
# Split into T dimension
sct.printv('\nSplit along T dimension...', param.verbose)
im_data = Image(file_data + ext_data)
im_data_split_list = split_data(im_data, 3)
for im in im_data_split_list:
im.save()
# assign an index to each volume
index_fmri = range(0, nt)
# Number of groups
nb_groups = int(math.floor(nt / param.group_size))
# Generate groups indexes
group_indexes = []
for iGroup in range(nb_groups):
group_indexes.append(index_fmri[(iGroup *
param.group_size):((iGroup + 1) *
param.group_size)])
# add the remaining images to the last DWI group
nb_remaining = nt % param.group_size # number of remaining images
if nb_remaining > 0:
nb_groups += 1
group_indexes.append(index_fmri[len(index_fmri) -
nb_remaining:len(index_fmri)])
# groups
for iGroup in range(nb_groups):
sct.printv('\nGroup: ' + str((iGroup + 1)) + '/' + str(nb_groups),
param.verbose)
# get index
index_fmri_i = group_indexes[iGroup]
nt_i = len(index_fmri_i)
# Merge Images
sct.printv('Merge consecutive volumes...', param.verbose)
file_data_merge_i = file_data + '_' + str(iGroup)
# cmd = fsloutput + 'fslmerge -t ' + file_data_merge_i
# for it in range(nt_i):
# cmd = cmd + ' ' + file_data + '_T' + str(index_fmri_i[it]).zfill(4)
im_fmri_list = []
for it in range(nt_i):
im_fmri_list.append(im_data_split_list[index_fmri_i[it]])
im_fmri_concat = concat_data(im_fmri_list, 3)
im_fmri_concat.setFileName(file_data_merge_i + ext_data)
im_fmri_concat.save()
# Average Images
sct.printv('Average volumes...', param.verbose)
file_data_mean = file_data + '_mean_' + str(iGroup)
sct.run('sct_maths -i ' + file_data_merge_i + '.nii -o ' +
file_data_mean + '.nii -mean t')
# if not average_data_across_dimension(file_data_merge_i+'.nii', file_data_mean+'.nii', 3):
# sct.printv('ERROR in average_data_across_dimension', 1, 'error')
# cmd = fsloutput + 'fslmaths ' + file_data_merge_i + ' -Tmean ' + file_data_mean
# sct.run(cmd, param.verbose)
# Merge groups means
sct.printv('\nMerging volumes...', param.verbose)
file_data_groups_means_merge = 'fmri_averaged_groups'
im_mean_list = []
for iGroup in range(nb_groups):
im_mean_list.append(
Image(file_data + '_mean_' + str(iGroup) + ext_data))
im_mean_concat = concat_data(im_mean_list, 3)
im_mean_concat.setFileName(file_data_groups_means_merge + ext_data)
im_mean_concat.save()
# Estimate moco
sct.printv(
'\n-------------------------------------------------------------------------------',
param.verbose)
sct.printv(' Estimating motion...', param.verbose)
sct.printv(
'-------------------------------------------------------------------------------',
param.verbose)
param_moco = param
param_moco.file_data = 'fmri_averaged_groups'
param_moco.file_target = file_data + '_mean_' + param.num_target
param_moco.path_out = ''
param_moco.todo = 'estimate_and_apply'
param_moco.mat_moco = 'mat_groups'
moco.moco(param_moco)
# create final mat folder
sct.create_folder(mat_final)
# Copy registration matrices
sct.printv('\nCopy transformations...', param.verbose)
for iGroup in range(nb_groups):
for data in range(len(group_indexes[iGroup])):
sct.run(
'cp ' + 'mat_groups/' + 'mat.T' + str(iGroup) + ext_mat + ' ' +
mat_final + 'mat.T' + str(group_indexes[iGroup][data]) +
ext_mat, param.verbose)
# Apply moco on all fmri data
sct.printv(
'\n-------------------------------------------------------------------------------',
param.verbose)
sct.printv(' Apply moco', param.verbose)
sct.printv(
'-------------------------------------------------------------------------------',
param.verbose)
param_moco.file_data = 'fmri'
param_moco.file_target = file_data + '_mean_' + str(0)
param_moco.path_out = ''
param_moco.mat_moco = mat_final
param_moco.todo = 'apply'
moco.moco(param_moco)
# copy geometric information from header
# NB: this is required because WarpImageMultiTransform in 2D mode wrongly sets pixdim(3) to "1".
im_fmri = Image('fmri.nii')
im_fmri_moco = Image('fmri_moco.nii')
im_fmri_moco = copy_header(im_fmri, im_fmri_moco)
im_fmri_moco.save()
# Average volumes
sct.printv('\nAveraging data...', param.verbose)
sct.run('sct_maths -i fmri_moco.nii -o fmri_moco_mean.nii -mean t')
def eddy_correct(param):
sct.printv('\n\n\n\n===================================================',param.verbose)
sct.printv(' Running: eddy_correct', param.verbose)
sct.printv('===================================================\n',param.verbose)
fname_data = param.fname_data
min_norm = param.min_norm
cost_function = param.cost_function_flirt
verbose = param.verbose
sct.printv(('Input File:'+ param.fname_data),verbose)
sct.printv(('Bvecs File:' + param.fname_bvecs),verbose)
#Extract path, file and extension
path_data, file_data, ext_data = sct.extract_fname(fname_data)
if param.mat_eddy=='': param.mat_eddy= 'mat_eddy/'
if not os.path.exists(param.mat_eddy): os.makedirs(param.mat_eddy)
mat_eddy = param.mat_eddy
#Schedule file for FLIRT
schedule_file = path_sct + '/flirtsch/schedule_TxTy_2mmScale.sch'
sct.printv(('\n.. Schedule file: '+ schedule_file),verbose)
#Swap X-Y dimension (to have X as phase-encoding direction)
if param.swapXY==1:
sct.printv('\nSwap X-Y dimension (to have X as phase-encoding direction)',verbose)
fname_data_new = 'tmp.data_swap'
cmd = fsloutput + 'fslswapdim ' + fname_data + ' -y -x -z ' + fname_data_new
status, output = sct.run(cmd,verbose)
sct.printv(('\n.. updated data file name: '+fname_data_new),verbose)
else:
fname_data_new = fname_data
# Get size of data
sct.printv('\nGet dimensions data...',verbose)
nx, ny, nz, nt, px, py, pz, pt = Image(fname_data).dim
sct.printv('.. '+str(nx)+' x '+str(ny)+' x '+str(nz)+' x '+str(nt),verbose)
# split along T dimension
sct.printv('\nSplit along T dimension...',verbose)
from sct_image import split_data
im_to_split = Image(fname_data_new+'.nii')
im_split_list = split_data(im_to_split, 3)
for im in im_split_list:
im.save()
# cmd = fsloutput + 'fslsplit ' + fname_data_new + ' ' + file_data + '_T'
# status, output = sct.run(cmd,verbose)
#Slice-wise or Volume based method
if param.slicewise:
nb_loops = nz
file_suffix=[]
for iZ in range(nz):
file_suffix.append('_Z'+ str(iZ).zfill(4))
else:
nb_loops = 1
file_suffix = ['']
# Identify pairs of opposite gradient directions
sct.printv('\nIdentify pairs of opposite gradient directions...',verbose)
# Open bvecs file
sct.printv('\nOpen bvecs file...',verbose)
bvecs = []
with open(param.fname_bvecs) as f:
for line in f:
bvecs_new = map(float, line.split())
bvecs.append(bvecs_new)
# Check if bvecs file is nx3
if not len(bvecs[0][:]) == 3:
sct.printv('.. WARNING: bvecs file is 3xn instead of nx3. Consider using sct_dmri_transpose_bvecs.',verbose)
sct.printv('Transpose bvecs...',verbose)
# transpose bvecs
bvecs = zip(*bvecs)
bvecs = np.array(bvecs)
opposite_gradients_iT = []
opposite_gradients_jT = []
index_identified = []
index_b0 = []
for iT in range(nt-1):
if np.linalg.norm(bvecs[iT,:])!=0:
if iT not in index_identified:
jT = iT+1
if np.linalg.norm((bvecs[iT,:]+bvecs[jT,:]))<min_norm:
sct.printv(('.. Opposite gradient for #'+str(iT)+' is: #'+str(jT)),verbose)
opposite_gradients_iT.append(iT)
opposite_gradients_jT.append(jT)
index_identified.append(iT)
else:
index_b0.append(iT)
sct.printv(('.. Opposite gradient for #'+str(iT)+' is: NONE (b=0)'),verbose)
nb_oppositeGradients = len(opposite_gradients_iT)
sct.printv(('.. Number of gradient directions: ' + str(2*nb_oppositeGradients) + ' (2*' + str(nb_oppositeGradients) + ')'),verbose)
sct.printv('.. Index b=0: '+ str(index_b0),verbose)
# =========================================================================
# Find transformation
# =========================================================================
for iN in range(nb_oppositeGradients):
i_plus = opposite_gradients_iT[iN]
i_minus = opposite_gradients_jT[iN]
sct.printv(('\nFinding affine transformation between volumes #'+str(i_plus)+' and #'+str(i_minus)+' (' + str(iN)+'/'+str(nb_oppositeGradients)+')'),verbose)
sct.printv('------------------------------------------------------------------------------------\n',verbose)
#Slicewise correction
if param.slicewise:
sct.printv('\nSplit volumes across Z...',verbose)
fname_plus = file_data + '_T' + str(i_plus).zfill(4)
fname_plus_Z = file_data + '_T' + str(i_plus).zfill(4) + '_Z'
im_plus = Image(fname_plus+'.nii')
im_plus_split_list = split_data(im_plus, 2)
for im_p in im_plus_split_list:
im_p.save()
# cmd = fsloutput + 'fslsplit ' + fname_plus + ' ' + fname_plus_Z + ' -z'
# status, output = sct.run(cmd,verbose)
fname_minus = file_data + '_T' + str(i_minus).zfill(4)
fname_minus_Z = file_data + '_T' + str(i_minus).zfill(4) + '_Z'
im_minus = Image(fname_minus+'.nii')
im_minus_split_list = split_data(im_minus, 2)
for im_m in im_minus_split_list:
im_m.save() # cmd = fsloutput + 'fslsplit ' + fname_minus + ' ' + fname_minus_Z + ' -z'
# status, output = sct.run(cmd,verbose)
#loop across Z
for iZ in range(nb_loops):
fname_plus = file_data + '_T' + str(i_plus).zfill(4) + file_suffix[iZ]
fname_minus = file_data + '_T' + str(i_minus).zfill(4) + file_suffix[iZ]
#Find transformation on opposite gradient directions
sct.printv('\nFind transformation for each pair of opposite gradient directions...',verbose)
fname_plus_corr = file_data + '_T' + str(i_plus).zfill(4) + file_suffix[iZ] + '_corr_'
omat = 'mat_' + file_data + '_T' + str(i_plus).zfill(4) + file_suffix[iZ] + '.txt'
cmd = fsloutput+'flirt -in '+fname_plus+' -ref '+fname_minus+' -paddingsize 3 -schedule '+schedule_file+' -verbose 2 -omat '+omat+' -cost '+cost_function+' -forcescaling'
status, output = sct.run(cmd,verbose)
file = open(omat)
Matrix = np.loadtxt(file)
file.close()
M = Matrix[0:4,0:4]
sct.printv(('.. Transformation matrix:\n'+str(M)),verbose)
sct.printv(('.. Output matrix file: '+omat),verbose)
# Divide affine transformation by two
sct.printv('\nDivide affine transformation by two...',verbose)
A = (M - np.identity(4))/2
Mplus = np.identity(4)+A
omat_plus = mat_eddy + 'mat.T' + str(i_plus) + '_Z' + str(iZ) + '.txt'
file = open(omat_plus,'w')
np.savetxt(omat_plus, Mplus, fmt='%.6e', delimiter=' ', newline='\n', header='', footer='', comments='#')
file.close()
sct.printv(('.. Output matrix file (plus): '+omat_plus),verbose)
Mminus = np.identity(4)-A
omat_minus = mat_eddy + 'mat.T' + str(i_minus) + '_Z' + str(iZ) + '.txt'
file = open(omat_minus,'w')
np.savetxt(omat_minus, Mminus, fmt='%.6e', delimiter=' ', newline='\n', header='', footer='', comments='#')
file.close()
sct.printv(('.. Output matrix file (minus): '+omat_minus),verbose)
# =========================================================================
# Apply affine transformation
# =========================================================================
sct.printv('\nApply affine transformation matrix',verbose)
sct.printv('------------------------------------------------------------------------------------\n',verbose)
for iN in range(nb_oppositeGradients):
for iFile in range(2):
if iFile==0:
i_file = opposite_gradients_iT[iN]
else:
i_file = opposite_gradients_jT[iN]
for iZ in range(nb_loops):
fname = file_data + '_T' + str(i_file).zfill(4) + file_suffix[iZ]
fname_corr = fname + '_corr_' + '__div2'
omat = mat_eddy + 'mat.T' + str(i_file) + '_Z' + str(iZ) + '.txt'
cmd = fsloutput + 'flirt -in ' + fname + ' -ref ' + fname + ' -out ' + fname_corr + ' -init ' + omat + ' -applyxfm -paddingsize 3 -interp ' + param.interp
status, output = sct.run(cmd,verbose)
# =========================================================================
# Merge back across Z
# =========================================================================
sct.printv('\nMerge across Z',verbose)
sct.printv('------------------------------------------------------------------------------------\n',verbose)
for iN in range(nb_oppositeGradients):
i_plus = opposite_gradients_iT[iN]
fname_plus_corr = file_data + '_T' + str(i_plus).zfill(4) + '_corr_' + '__div2'
cmd = fsloutput + 'fslmerge -z ' + fname_plus_corr
for iZ in range(nz):
fname_plus_Z_corr = file_data + '_T' + str(i_plus).zfill(4) + file_suffix[iZ] + '_corr_' + '__div2'
cmd = cmd + ' ' + fname_plus_Z_corr
status, output = sct.run(cmd,verbose)
i_minus = opposite_gradients_jT[iN]
fname_minus_corr = file_data + '_T' + str(i_minus).zfill(4) + '_corr_' + '__div2'
cmd = fsloutput + 'fslmerge -z ' + fname_minus_corr
for iZ in range(nz):
fname_minus_Z_corr = file_data + '_T' + str(i_minus).zfill(4) + file_suffix[iZ] + '_corr_' + '__div2'
cmd = cmd + ' ' + fname_minus_Z_corr
status, output = sct.run(cmd,verbose)
# =========================================================================
# Merge files back
# =========================================================================
sct.printv('\nMerge back across T...',verbose)
sct.printv('------------------------------------------------------------------------------------\n',verbose)
fname_data_corr = param.output_path + file_data + '_eddy'
cmd = fsloutput + 'fslmerge -t ' + fname_data_corr
path_tmp = os.getcwd()
for iT in range(nt):
if os.path.isfile((path_tmp + '/' + file_data + '_T' + str(iT).zfill(4) + '_corr_' + '__div2.nii')):
fname_data_corr_3d = file_data + '_T' + str(iT).zfill(4) + '_corr_' + '__div2'
elif iT in index_b0:
fname_data_corr_3d = file_data + '_T' + str(iT).zfill(4)
cmd = cmd + ' ' + fname_data_corr_3d
status, output = sct.run(cmd,verbose)
#Swap back X-Y dimensions
if param.swapXY==1:
fname_data_final = fname_data
sct.printv('\nSwap back X-Y dimensions',verbose)
cmd = fsloutput_temp + 'fslswapdim ' + fname_data_corr + ' -y -x -z ' + fname_data_final
status, output = sct.run(cmd,verbose)
else:
fname_data_final = fname_data_corr
sct.printv(('... File created: '+fname_data_final),verbose)
sct.printv('\n===================================================',verbose)
sct.printv(' Completed: eddy_correct',verbose)
sct.printv('===================================================\n\n\n',verbose)
def moco(param):
# retrieve parameters
fsloutput = 'export FSLOUTPUTTYPE=NIFTI; ' # for faster processing, all outputs are in NIFTI
file_data = param.file_data
file_target = param.file_target
folder_mat = sct.slash_at_the_end(param.mat_moco, 1) # output folder of mat file
todo = param.todo
suffix = param.suffix
#file_schedule = param.file_schedule
verbose = param.verbose
ext = '.nii'
# get path of the toolbox
status, path_sct = commands.getstatusoutput('echo $SCT_DIR')
# print arguments
sct.printv('\nInput parameters:', param.verbose)
sct.printv(' Input file ............' + file_data, param.verbose)
sct.printv(' Reference file ........' + file_target, param.verbose)
sct.printv(' Polynomial degree .....' + param.param[0], param.verbose)
sct.printv(' Smoothing kernel ......' + param.param[1], param.verbose)
sct.printv(' Gradient step .........' + param.param[2], param.verbose)
sct.printv(' Metric ................' + param.param[3], param.verbose)
sct.printv(' Todo ..................' + todo, param.verbose)
sct.printv(' Mask .................' + param.fname_mask, param.verbose)
sct.printv(' Output mat folder .....' + folder_mat, param.verbose)
# create folder for mat files
sct.create_folder(folder_mat)
# Get size of data
sct.printv('\nGet dimensions data...', verbose)
data_im = Image(file_data + ext)
nx, ny, nz, nt, px, py, pz, pt = data_im.dim
sct.printv(('.. ' + str(nx) + ' x ' + str(ny) + ' x ' + str(nz) + ' x ' + str(nt)), verbose)
# copy file_target to a temporary file
sct.printv('\nCopy file_target to a temporary file...', verbose)
sct.run('cp ' + file_target + ext + ' target.nii')
file_target = 'target'
# Split data along T dimension
sct.printv('\nSplit data along T dimension...', verbose)
data_split_list = split_data(data_im, dim=3)
for im in data_split_list:
im.save()
file_data_splitT = file_data + '_T'
# Motion correction: initialization
index = np.arange(nt)
file_data_splitT_num = []
file_data_splitT_moco_num = []
failed_transfo = [0 for i in range(nt)]
file_mat = [[] for i in range(nt)]
# Motion correction: Loop across T
for indice_index in range(nt):
# create indices and display stuff
it = index[indice_index]
file_data_splitT_num.append(file_data_splitT + str(it).zfill(4))
file_data_splitT_moco_num.append(file_data + suffix + '_T' + str(it).zfill(4))
sct.printv(('\nVolume ' + str((it)) + '/' + str(nt - 1) + ':'), verbose)
file_mat[it] = folder_mat + 'mat.T' + str(it)
# run 3D registration
failed_transfo[it] = register(param, file_data_splitT_num[it], file_target, file_mat[it], file_data_splitT_moco_num[it])
# average registered volume with target image
# N.B. use weighted averaging: (target * nb_it + moco) / (nb_it + 1)
if param.iterative_averaging and indice_index < 10 and failed_transfo[it] == 0:
sct.run('sct_maths -i ' + file_target + ext + ' -mul ' + str(indice_index + 1) + ' -o ' + file_target + ext)
sct.run('sct_maths -i ' + file_target + ext + ' -add ' + file_data_splitT_moco_num[it] + ext + ' -o ' + file_target + ext)
sct.run('sct_maths -i ' + file_target + ext + ' -div ' + str(indice_index + 2) + ' -o ' + file_target + ext)
# Replace failed transformation with the closest good one
sct.printv(('\nReplace failed transformations...'), verbose)
fT = [i for i, j in enumerate(failed_transfo) if j == 1]
gT = [i for i, j in enumerate(failed_transfo) if j == 0]
for it in range(len(fT)):
abs_dist = [abs(gT[i] - fT[it]) for i in range(len(gT))]
if not abs_dist == []:
index_good = abs_dist.index(min(abs_dist))
sct.printv(' transfo #' + str(fT[it]) + ' --> use transfo #' + str(gT[index_good]), verbose)
# copy transformation
sct.run('cp ' + file_mat[gT[index_good]] + 'Warp.nii.gz' + ' ' + file_mat[fT[it]] + 'Warp.nii.gz')
# apply transformation
sct.run('sct_apply_transfo -i ' + file_data_splitT_num[fT[it]] + '.nii -d ' + file_target + '.nii -w ' + file_mat[fT[it]] + 'Warp.nii.gz' + ' -o ' + file_data_splitT_moco_num[fT[it]] + '.nii' + ' -x ' + param.interp, verbose)
else:
# exit program if no transformation exists.
sct.printv('\nERROR in ' + os.path.basename(__file__) + ': No good transformation exist. Exit program.\n', verbose, 'error')
sys.exit(2)
# Merge data along T
file_data_moco = file_data + suffix
if todo != 'estimate':
sct.printv('\nMerge data back along T...', verbose)
from sct_image import concat_data
# im_list = []
fname_list = []
for indice_index in range(len(index)):
# im_list.append(Image(file_data_splitT_moco_num[indice_index] + ext))
fname_list.append(file_data_splitT_moco_num[indice_index] + ext)
im_out = concat_data(fname_list, 3)
im_out.setFileName(file_data_moco + ext)
im_out.save()
# delete file target.nii (to avoid conflict if this function is run another time)
sct.printv('\nRemove temporary file...', verbose)
# os.remove('target.nii')
sct.run('rm target.nii')
def fmri_moco(param):
file_data = "fmri.nii"
mat_final = 'mat_final/'
ext_mat = 'Warp.nii.gz' # warping field
# Get dimensions of data
sct.printv('\nGet dimensions of data...', param.verbose)
im_data = Image(param.fname_data)
nx, ny, nz, nt, px, py, pz, pt = im_data.dim
sct.printv(
' ' + str(nx) + ' x ' + str(ny) + ' x ' + str(nz) + ' x ' + str(nt),
param.verbose)
# Get orientation
sct.printv('\nData orientation: ' + im_data.orientation, param.verbose)
if im_data.orientation[2] in 'LR':
param.is_sagittal = True
sct.printv(' Treated as sagittal')
elif im_data.orientation[2] in 'IS':
param.is_sagittal = False
sct.printv(' Treated as axial')
else:
param.is_sagittal = False
sct.printv(
'WARNING: Orientation seems to be neither axial nor sagittal.')
# Adjust group size in case of sagittal scan
if param.is_sagittal and param.group_size != 1:
sct.printv(
'For sagittal data group_size should be one for more robustness. Forcing group_size=1.',
1, 'warning')
param.group_size = 1
# Split into T dimension
sct.printv('\nSplit along T dimension...', param.verbose)
im_data_split_list = split_data(im_data, 3)
for im in im_data_split_list:
x_dirname, x_basename, x_ext = sct.extract_fname(im.absolutepath)
# Make further steps slurp the data to avoid too many open files (#2149)
im.absolutepath = os.path.join(x_dirname, x_basename + ".nii.gz")
im.save()
# assign an index to each volume
index_fmri = list(range(0, nt))
# Number of groups
nb_groups = int(math.floor(nt / param.group_size))
# Generate groups indexes
group_indexes = []
for iGroup in range(nb_groups):
group_indexes.append(index_fmri[(iGroup *
param.group_size):((iGroup + 1) *
param.group_size)])
# add the remaining images to the last fMRI group
nb_remaining = nt % param.group_size # number of remaining images
if nb_remaining > 0:
nb_groups += 1
group_indexes.append(index_fmri[len(index_fmri) -
nb_remaining:len(index_fmri)])
# groups
for iGroup in tqdm(range(nb_groups),
unit='iter',
unit_scale=False,
desc="Merge within groups",
ascii=True,
ncols=80):
# get index
index_fmri_i = group_indexes[iGroup]
nt_i = len(index_fmri_i)
# Merge Images
file_data_merge_i = sct.add_suffix(file_data, '_' + str(iGroup))
# cmd = fsloutput + 'fslmerge -t ' + file_data_merge_i
# for it in range(nt_i):
# cmd = cmd + ' ' + file_data + '_T' + str(index_fmri_i[it]).zfill(4)
im_fmri_list = []
for it in range(nt_i):
im_fmri_list.append(im_data_split_list[index_fmri_i[it]])
im_fmri_concat = concat_data(im_fmri_list, 3,
squeeze_data=True).save(file_data_merge_i)
file_data_mean = sct.add_suffix(file_data, '_mean_' + str(iGroup))
if file_data_mean.endswith(".nii"):
file_data_mean += ".gz" # #2149
if param.group_size == 1:
# copy to new file name instead of averaging (faster)
# note: this is a bandage. Ideally we should skip this entire for loop if g=1
convert(file_data_merge_i, file_data_mean)
else:
# Average Images
sct.run([
'sct_maths', '-i', file_data_merge_i, '-o', file_data_mean,
'-mean', 't'
],
verbose=0)
# if not average_data_across_dimension(file_data_merge_i+'.nii', file_data_mean+'.nii', 3):
# sct.printv('ERROR in average_data_across_dimension', 1, 'error')
# cmd = fsloutput + 'fslmaths ' + file_data_merge_i + ' -Tmean ' + file_data_mean
# sct.run(cmd, param.verbose)
# Merge groups means. The output 4D volume will be used for motion correction.
sct.printv('\nMerging volumes...', param.verbose)
file_data_groups_means_merge = 'fmri_averaged_groups.nii'
im_mean_list = []
for iGroup in range(nb_groups):
file_data_mean = sct.add_suffix(file_data, '_mean_' + str(iGroup))
if file_data_mean.endswith(".nii"):
file_data_mean += ".gz" # #2149
im_mean_list.append(Image(file_data_mean))
im_mean_concat = concat_data(im_mean_list,
3).save(file_data_groups_means_merge)
# Estimate moco
sct.printv(
'\n-------------------------------------------------------------------------------',
param.verbose)
sct.printv(' Estimating motion...', param.verbose)
sct.printv(
'-------------------------------------------------------------------------------',
param.verbose)
param_moco = param
param_moco.file_data = 'fmri_averaged_groups.nii'
param_moco.file_target = sct.add_suffix(file_data,
'_mean_' + param.num_target)
if param_moco.file_target.endswith(".nii"):
param_moco.file_target += ".gz" # #2149
param_moco.path_out = ''
param_moco.todo = 'estimate_and_apply'
param_moco.mat_moco = 'mat_groups'
file_mat = moco.moco(param_moco)
# TODO: if g=1, no need to run the block below (already applied)
if param.group_size == 1:
# if flag g=1, it means that all images have already been corrected, so we just need to rename the file
sct.mv('fmri_averaged_groups_moco.nii', 'fmri_moco.nii')
else:
# create final mat folder
sct.create_folder(mat_final)
# Copy registration matrices
sct.printv('\nCopy transformations...', param.verbose)
for iGroup in range(nb_groups):
for data in range(
len(group_indexes[iGroup])
): # we cannot use enumerate because group_indexes has 2 dim.
# fetch all file_mat_z for given t-group
list_file_mat_z = file_mat[:, iGroup]
# loop across file_mat_z and copy to mat_final folder
for file_mat_z in list_file_mat_z:
# we want to copy 'mat_groups/mat.ZXXXXTYYYYWarp.nii.gz' --> 'mat_final/mat.ZXXXXTYYYZWarp.nii.gz'
# Notice the Y->Z in the under the T index: the idea here is to use the single matrix from each group,
# and apply it to all images belonging to the same group.
sct.copy(
file_mat_z + ext_mat,
mat_final + file_mat_z[11:20] + 'T' +
str(group_indexes[iGroup][data]).zfill(4) + ext_mat)
# Apply moco on all fmri data
sct.printv(
'\n-------------------------------------------------------------------------------',
param.verbose)
sct.printv(' Apply moco', param.verbose)
sct.printv(
'-------------------------------------------------------------------------------',
param.verbose)
param_moco.file_data = 'fmri.nii'
param_moco.file_target = sct.add_suffix(file_data, '_mean_' + str(0))
if param_moco.file_target.endswith(".nii"):
param_moco.file_target += ".gz"
param_moco.path_out = ''
param_moco.mat_moco = mat_final
param_moco.todo = 'apply'
file_mat = moco.moco(param_moco)
# copy geometric information from header
# NB: this is required because WarpImageMultiTransform in 2D mode wrongly sets pixdim(3) to "1".
im_fmri = Image('fmri.nii')
im_fmri_moco = Image('fmri_moco.nii')
im_fmri_moco.header = im_fmri.header
im_fmri_moco.save()
# Extract and output the motion parameters
if param.output_motion_param:
from sct_image import multicomponent_split
import csv
#files_warp = []
files_warp_X, files_warp_Y = [], []
moco_param = []
for fname_warp in file_mat[0]:
# Cropping the image to keep only one voxel in the XY plane
im_warp = Image(fname_warp + ext_mat)
im_warp.data = np.expand_dims(np.expand_dims(
im_warp.data[0, 0, :, :, :], axis=0),
axis=0)
# These three lines allow to generate one file instead of two, containing X, Y and Z moco parameters
#fname_warp_crop = fname_warp + '_crop_' + ext_mat
#files_warp.append(fname_warp_crop)
#im_warp.save(fname_warp_crop)
# Separating the three components and saving X and Y only (Z is equal to 0 by default).
im_warp_XYZ = multicomponent_split(im_warp)
fname_warp_crop_X = fname_warp + '_crop_X_' + ext_mat
im_warp_XYZ[0].save(fname_warp_crop_X)
files_warp_X.append(fname_warp_crop_X)
fname_warp_crop_Y = fname_warp + '_crop_Y_' + ext_mat
im_warp_XYZ[1].save(fname_warp_crop_Y)
files_warp_Y.append(fname_warp_crop_Y)
# Calculating the slice-wise average moco estimate to provide a QC file
moco_param.append([
np.mean(np.ravel(im_warp_XYZ[0].data)),
np.mean(np.ravel(im_warp_XYZ[1].data))
])
# These two lines allow to generate one file instead of two, containing X, Y and Z moco parameters
#im_warp_concat = concat_data(files_warp, dim=3)
#im_warp_concat.save('fmri_moco_params.nii')
# Concatenating the moco parameters into a time series for X and Y components.
im_warp_concat = concat_data(files_warp_X, dim=3)
im_warp_concat.save('fmri_moco_params_X.nii')
im_warp_concat = concat_data(files_warp_Y, dim=3)
im_warp_concat.save('fmri_moco_params_Y.nii')
# Writing a TSV file with the slicewise average estimate of the moco parameters, as it is a useful QC file.
with open('fmri_moco_params.tsv', 'wt') as out_file:
tsv_writer = csv.writer(out_file, delimiter='\t')
tsv_writer.writerow(['X', 'Y'])
for mocop in moco_param:
tsv_writer.writerow([mocop[0], mocop[1]])
# Average volumes
sct.printv('\nAveraging data...', param.verbose)
sct_maths.main(args=[
'-i', 'fmri_moco.nii', '-o', 'fmri_moco_mean.nii', '-mean', 't', '-v',
'0'
])
def register2d_columnwise(fname_src, fname_dest, fname_warp='warp_forward.nii.gz', fname_warp_inv='warp_inverse.nii.gz', verbose=0, path_qc='./', smoothWarpXY=1):
"""
Column-wise non-linear registration of segmentations. Based on an idea from Allan Martin.
- Assumes src/dest are segmentations (not necessarily binary), and already registered by center of mass
- Assumes src/dest are in RPI orientation.
- Split along Z, then for each slice:
- scale in R-L direction to match src/dest
- loop across R-L columns and register by (i) matching center of mass and (ii) scaling.
:param fname_src:
:param fname_dest:
:param fname_warp:
:param fname_warp_inv:
:param verbose:
:return:
"""
# initialization
th_nonzero = 0.5 # values below are considered zero
# for display stuff
if verbose == 2:
import matplotlib
matplotlib.use('Agg') # prevent display figure
import matplotlib.pyplot as plt
# Get image dimensions and retrieve nz
sct.printv('\nGet image dimensions of destination image...', verbose)
nx, ny, nz, nt, px, py, pz, pt = Image(fname_dest).dim
sct.printv(' matrix size: '+str(nx)+' x '+str(ny)+' x '+str(nz), verbose)
sct.printv(' voxel size: '+str(px)+'mm x '+str(py)+'mm x '+str(pz)+'mm', verbose)
# Split source volume along z
sct.printv('\nSplit input volume...', verbose)
from sct_image import split_data
im_src = Image('src.nii')
split_source_list = split_data(im_src, 2)
for im in split_source_list:
im.save()
# Split destination volume along z
sct.printv('\nSplit destination volume...', verbose)
im_dest = Image('dest.nii')
split_dest_list = split_data(im_dest, 2)
for im in split_dest_list:
im.save()
# open image
data_src = im_src.data
data_dest = im_dest.data
if len(data_src.shape) == 2:
# reshape 2D data into pseudo 3D (only one slice)
new_shape = list(data_src.shape)
new_shape.append(1)
new_shape = tuple(new_shape)
data_src = data_src.reshape(new_shape)
data_dest = data_dest.reshape(new_shape)
# initialize forward warping field (defined in destination space)
warp_x = np.zeros(data_dest.shape)
warp_y = np.zeros(data_dest.shape)
# initialize inverse warping field (defined in source space)
warp_inv_x = np.zeros(data_src.shape)
warp_inv_y = np.zeros(data_src.shape)
# Loop across slices
sct.printv('\nEstimate columnwise transformation...', verbose)
for iz in range(0, nz):
print str(iz)+'/'+str(nz)+'..',
# PREPARE COORDINATES
# ============================================================
# get indices of x and y coordinates
row, col = np.indices((nx, ny))
# build 2xn array of coordinates in pixel space
# ordering of indices is as follows:
# coord_init_pix[:, 0] = 0, 0, 0, ..., 1, 1, 1..., nx, nx, nx
# coord_init_pix[:, 1] = 0, 1, 2, ..., 0, 1, 2..., 0, 1, 2
coord_init_pix = np.array([row.ravel(), col.ravel(), np.array(np.ones(len(row.ravel())) * iz)]).T
# convert coordinates to physical space
coord_init_phy = np.array(im_src.transfo_pix2phys(coord_init_pix))
# get 2d data from the selected slice
src2d = data_src[:, :, iz]
dest2d = data_dest[:, :, iz]
# julien 20161105
#<<<
# threshold at 0.5
src2d[src2d < th_nonzero] = 0
dest2d[dest2d < th_nonzero] = 0
# get non-zero coordinates, and transpose to obtain nx2 dimensions
coord_src2d = np.array(np.where(src2d > 0)).T
coord_dest2d = np.array(np.where(dest2d > 0)).T
# here we use 0.5 as threshold for non-zero value
# coord_src2d = np.array(np.where(src2d > th_nonzero)).T
# coord_dest2d = np.array(np.where(dest2d > th_nonzero)).T
#>>>
# SCALING R-L (X dimension)
# ============================================================
# sum data across Y to obtain 1D signal: src_y and dest_y
src1d = np.sum(src2d, 1)
dest1d = np.sum(dest2d, 1)
# make sure there are non-zero data in src or dest
if np.any(src1d > th_nonzero) and np.any(dest1d > th_nonzero):
# retrieve min/max of non-zeros elements (edge of the segmentation)
# julien 20161105
# <<<
src1d_min, src1d_max = min(np.where(src1d != 0)[0]), max(np.where(src1d != 0)[0])
dest1d_min, dest1d_max = min(np.where(dest1d != 0)[0]), max(np.where(dest1d != 0)[0])
# for i in xrange(len(src1d)):
# if src1d[i] > 0.5:
# found index above 0.5, exit loop
# break
# get indices (in continuous space) at half-maximum of upward and downward slope
# src1d_min, src1d_max = find_index_halfmax(src1d)
# dest1d_min, dest1d_max = find_index_halfmax(dest1d)
# >>>
# 1D matching between src_y and dest_y
mean_dest_x = (dest1d_max + dest1d_min) / 2
mean_src_x = (src1d_max + src1d_min) / 2
# compute x-scaling factor
Sx = (dest1d_max - dest1d_min + 1) / float(src1d_max - src1d_min + 1)
# apply transformation to coordinates
coord_src2d_scaleX = np.copy(coord_src2d) # need to use np.copy to avoid copying pointer
coord_src2d_scaleX[:, 0] = (coord_src2d[:, 0] - mean_src_x) * Sx + mean_dest_x
coord_init_pix_scaleX = np.copy(coord_init_pix)
coord_init_pix_scaleX[:, 0] = (coord_init_pix[:, 0] - mean_src_x) * Sx + mean_dest_x
coord_init_pix_scaleXinv = np.copy(coord_init_pix)
coord_init_pix_scaleXinv[:, 0] = (coord_init_pix[:, 0] - mean_dest_x) / float(Sx) + mean_src_x
# apply transformation to image
from skimage.transform import warp
row_scaleXinv = np.reshape(coord_init_pix_scaleXinv[:, 0], [nx, ny])
src2d_scaleX = warp(src2d, np.array([row_scaleXinv, col]), order=1)
# ============================================================
# COLUMN-WISE REGISTRATION (Y dimension for each Xi)
# ============================================================
coord_init_pix_scaleY = np.copy(coord_init_pix) # need to use np.copy to avoid copying pointer
coord_init_pix_scaleYinv = np.copy(coord_init_pix) # need to use np.copy to avoid copying pointer
# coord_src2d_scaleXY = np.copy(coord_src2d_scaleX) # need to use np.copy to avoid copying pointer
# loop across columns (X dimension)
for ix in xrange(nx):
# retrieve 1D signal along Y
src1d = src2d_scaleX[ix, :]
dest1d = dest2d[ix, :]
# make sure there are non-zero data in src or dest
if np.any(src1d>th_nonzero) and np.any(dest1d>th_nonzero):
# retrieve min/max of non-zeros elements (edge of the segmentation)
# src1d_min, src1d_max = min(np.nonzero(src1d)[0]), max(np.nonzero(src1d)[0])
# dest1d_min, dest1d_max = min(np.nonzero(dest1d)[0]), max(np.nonzero(dest1d)[0])
# 1D matching between src_y and dest_y
# Ty = (dest1d_max + dest1d_min)/2 - (src1d_max + src1d_min)/2
# Sy = (dest1d_max - dest1d_min) / float(src1d_max - src1d_min)
# apply translation and scaling to coordinates in column
# get indices (in continuous space) at half-maximum of upward and downward slope
# src1d_min, src1d_max = find_index_halfmax(src1d)
# dest1d_min, dest1d_max = find_index_halfmax(dest1d)
src1d_min, src1d_max = np.min(np.where(src1d > th_nonzero)), np.max(np.where(src1d > th_nonzero))
dest1d_min, dest1d_max = np.min(np.where(dest1d > th_nonzero)), np.max(np.where(dest1d > th_nonzero))
# 1D matching between src_y and dest_y
mean_dest_y = (dest1d_max + dest1d_min) / 2
mean_src_y = (src1d_max + src1d_min) / 2
# Tx = (dest1d_max + dest1d_min)/2 - (src1d_max + src1d_min)/2
Sy = (dest1d_max - dest1d_min + 1) / float(src1d_max - src1d_min + 1)
# apply forward transformation (in pixel space)
# below: only for debugging purpose
# coord_src2d_scaleX = np.copy(coord_src2d) # need to use np.copy to avoid copying pointer
# coord_src2d_scaleX[:, 0] = (coord_src2d[:, 0] - mean_src) * Sx + mean_dest
# coord_init_pix_scaleY = np.copy(coord_init_pix) # need to use np.copy to avoid copying pointer
# coord_init_pix_scaleY[:, 0] = (coord_init_pix[:, 0] - mean_src ) * Sx + mean_dest
range_x = range(ix * ny, ix * ny + nx)
coord_init_pix_scaleY[range_x, 1] = (coord_init_pix[range_x, 1] - mean_src_y) * Sy + mean_dest_y
coord_init_pix_scaleYinv[range_x, 1] = (coord_init_pix[range_x, 1] - mean_dest_y) / float(Sy) + mean_src_y
# apply transformation to image
col_scaleYinv = np.reshape(coord_init_pix_scaleYinv[:, 1], [nx, ny])
src2d_scaleXY = warp(src2d, np.array([row_scaleXinv, col_scaleYinv]), order=1)
# regularize Y warping fields
from skimage.filters import gaussian
col_scaleY = np.reshape(coord_init_pix_scaleY[:, 1], [nx, ny])
col_scaleYsmooth = gaussian(col_scaleY, smoothWarpXY)
col_scaleYinvsmooth = gaussian(col_scaleYinv, smoothWarpXY)
# apply smoothed transformation to image
src2d_scaleXYsmooth = warp(src2d, np.array([row_scaleXinv, col_scaleYinvsmooth]), order=1)
# reshape warping field as 1d
coord_init_pix_scaleY[:, 1] = col_scaleYsmooth.ravel()
coord_init_pix_scaleYinv[:, 1] = col_scaleYinvsmooth.ravel()
# display
if verbose == 2:
# FIG 1
plt.figure(figsize=(15, 3))
# plot #1
ax = plt.subplot(141)
plt.imshow(np.swapaxes(src2d, 1, 0), cmap=plt.cm.gray, interpolation='none')
plt.hold(True) # add other layer
plt.imshow(np.swapaxes(dest2d, 1, 0), cmap=plt.cm.copper, interpolation='none', alpha=0.5)
plt.title('src')
plt.xlabel('x')
plt.ylabel('y')
plt.xlim(mean_dest_x - 15, mean_dest_x + 15)
plt.ylim(mean_dest_y - 15, mean_dest_y + 15)
ax.grid(True, color='w')
# plot #2
ax = plt.subplot(142)
plt.imshow(np.swapaxes(src2d_scaleX, 1, 0), cmap=plt.cm.gray, interpolation='none')
plt.hold(True) # add other layer
plt.imshow(np.swapaxes(dest2d, 1, 0), cmap=plt.cm.copper, interpolation='none', alpha=0.5)
plt.title('src_scaleX')
plt.xlabel('x')
plt.ylabel('y')
plt.xlim(mean_dest_x - 15, mean_dest_x + 15)
plt.ylim(mean_dest_y - 15, mean_dest_y + 15)
ax.grid(True, color='w')
# plot #3
ax = plt.subplot(143)
plt.imshow(np.swapaxes(src2d_scaleXY, 1, 0), cmap=plt.cm.gray, interpolation='none')
plt.hold(True) # add other layer
plt.imshow(np.swapaxes(dest2d, 1, 0), cmap=plt.cm.copper, interpolation='none', alpha=0.5)
plt.title('src_scaleXY')
plt.xlabel('x')
plt.ylabel('y')
plt.xlim(mean_dest_x - 15, mean_dest_x + 15)
plt.ylim(mean_dest_y - 15, mean_dest_y + 15)
ax.grid(True, color='w')
# plot #4
ax = plt.subplot(144)
plt.imshow(np.swapaxes(src2d_scaleXYsmooth, 1, 0), cmap=plt.cm.gray, interpolation='none')
plt.hold(True) # add other layer
plt.imshow(np.swapaxes(dest2d, 1, 0), cmap=plt.cm.copper, interpolation='none', alpha=0.5)
plt.title('src_scaleXYsmooth (s='+str(smoothWarpXY)+')')
plt.xlabel('x')
plt.ylabel('y')
plt.xlim(mean_dest_x - 15, mean_dest_x + 15)
plt.ylim(mean_dest_y - 15, mean_dest_y + 15)
ax.grid(True, color='w')
# save figure
plt.savefig(path_qc + 'register2d_columnwise_image_z' + str(iz) + '.png')
plt.close()
# ============================================================
# CALCULATE TRANSFORMATIONS
# ============================================================
# calculate forward transformation (in physical space)
coord_init_phy_scaleX = np.array(im_dest.transfo_pix2phys(coord_init_pix_scaleX))
coord_init_phy_scaleY = np.array(im_dest.transfo_pix2phys(coord_init_pix_scaleY))
# calculate inverse transformation (in physical space)
coord_init_phy_scaleXinv = np.array(im_src.transfo_pix2phys(coord_init_pix_scaleXinv))
coord_init_phy_scaleYinv = np.array(im_src.transfo_pix2phys(coord_init_pix_scaleYinv))
# compute displacement per pixel in destination space (for forward warping field)
warp_x[:, :, iz] = np.array([coord_init_phy_scaleXinv[i, 0] - coord_init_phy[i, 0] for i in xrange(nx*ny)]).reshape((nx, ny))
warp_y[:, :, iz] = np.array([coord_init_phy_scaleYinv[i, 1] - coord_init_phy[i, 1] for i in xrange(nx*ny)]).reshape((nx, ny))
# compute displacement per pixel in source space (for inverse warping field)
warp_inv_x[:, :, iz] = np.array([coord_init_phy_scaleX[i, 0] - coord_init_phy[i, 0] for i in xrange(nx*ny)]).reshape((nx, ny))
warp_inv_y[:, :, iz] = np.array([coord_init_phy_scaleY[i, 1] - coord_init_phy[i, 1] for i in xrange(nx*ny)]).reshape((nx, ny))
# Generate forward warping field (defined in destination space)
generate_warping_field(fname_dest, warp_x, warp_y, fname_warp, verbose)
# Generate inverse warping field (defined in source space)
generate_warping_field(fname_src, warp_inv_x, warp_inv_y, fname_warp_inv, verbose)
def dmri_moco(param):
file_data = 'dmri'
ext_data = '.nii'
file_b0 = 'b0'
file_dwi = 'dwi'
mat_final = 'mat_final/'
file_dwi_group = 'dwi_averaged_groups' # no extension
fsloutput = 'export FSLOUTPUTTYPE=NIFTI; ' # for faster processing, all outputs are in NIFTI
ext_mat = 'Warp.nii.gz' # warping field
# Get dimensions of data
sct.printv('\nGet dimensions of data...', param.verbose)
im_data = Image(file_data + ext_data)
nx, ny, nz, nt, px, py, pz, pt = im_data.dim
sct.printv(' ' + str(nx) + ' x ' + str(ny) + ' x ' + str(nz), param.verbose)
# Identify b=0 and DWI images
sct.printv('\nIdentify b=0 and DWI images...', param.verbose)
index_b0, index_dwi, nb_b0, nb_dwi = identify_b0('bvecs.txt', param.fname_bvals, param.bval_min, param.verbose)
# check if dmri and bvecs are the same size
if not nb_b0 + nb_dwi == nt:
sct.printv('\nERROR in '+os.path.basename(__file__)+': Size of data ('+str(nt)+') and size of bvecs ('+str(nb_b0+nb_dwi)+') are not the same. Check your bvecs file.\n', 1, 'error')
sys.exit(2)
# Prepare NIFTI (mean/groups...)
#===================================================================================================================
# Split into T dimension
sct.printv('\nSplit along T dimension...', param.verbose)
im_data_split_list = split_data(im_data, 3)
for im in im_data_split_list:
im.save()
# Merge b=0 images
sct.printv('\nMerge b=0...', param.verbose)
# cmd = fsloutput + 'fslmerge -t ' + file_b0
# for it in range(nb_b0):
# cmd = cmd + ' ' + file_data + '_T' + str(index_b0[it]).zfill(4)
im_b0_list = []
for it in range(nb_b0):
im_b0_list.append(im_data_split_list[index_b0[it]])
im_b0_out = concat_data(im_b0_list, 3)
im_b0_out.setFileName(file_b0 + ext_data)
im_b0_out.save()
sct.printv((' File created: ' + file_b0), param.verbose)
# Average b=0 images
sct.printv('\nAverage b=0...', param.verbose)
file_b0_mean = file_b0+'_mean'
sct.run('sct_maths -i '+file_b0+ext_data+' -o '+file_b0_mean+ext_data+' -mean t', param.verbose)
# if not average_data_across_dimension(file_b0+'.nii', file_b0_mean+'.nii', 3):
# sct.printv('ERROR in average_data_across_dimension', 1, 'error')
# cmd = fsloutput + 'fslmaths ' + file_b0 + ' -Tmean ' + file_b0_mean
# status, output = sct.run(cmd, param.verbose)
# Number of DWI groups
nb_groups = int(math.floor(nb_dwi/param.group_size))
# Generate groups indexes
group_indexes = []
for iGroup in range(nb_groups):
group_indexes.append(index_dwi[(iGroup*param.group_size):((iGroup+1)*param.group_size)])
# add the remaining images to the last DWI group
nb_remaining = nb_dwi%param.group_size # number of remaining images
if nb_remaining > 0:
nb_groups += 1
group_indexes.append(index_dwi[len(index_dwi)-nb_remaining:len(index_dwi)])
# DWI groups
file_dwi_mean = []
for iGroup in range(nb_groups):
sct.printv('\nDWI group: ' +str((iGroup+1))+'/'+str(nb_groups), param.verbose)
# get index
index_dwi_i = group_indexes[iGroup]
nb_dwi_i = len(index_dwi_i)
# Merge DW Images
sct.printv('Merge DW images...', param.verbose)
file_dwi_merge_i = file_dwi + '_' + str(iGroup)
im_dwi_list = []
for it in range(nb_dwi_i):
im_dwi_list.append(im_data_split_list[index_dwi_i[it]])
im_dwi_out = concat_data(im_dwi_list, 3)
im_dwi_out.setFileName(file_dwi_merge_i + ext_data)
im_dwi_out.save()
# Average DW Images
sct.printv('Average DW images...', param.verbose)
file_dwi_mean.append(file_dwi + '_mean_' + str(iGroup))
sct.run('sct_maths -i '+file_dwi_merge_i+ext_data+' -o '+file_dwi_mean[iGroup]+ext_data+' -mean t', param.verbose)
# Merge DWI groups means
sct.printv('\nMerging DW files...', param.verbose)
# file_dwi_groups_means_merge = 'dwi_averaged_groups'
im_dw_list = []
for iGroup in range(nb_groups):
im_dw_list.append(Image(file_dwi_mean[iGroup] + ext_data))
im_dw_out = concat_data(im_dw_list, 3)
im_dw_out.setFileName(file_dwi_group + ext_data)
im_dw_out.save()
# cmd = fsloutput + 'fslmerge -t ' + file_dwi_group
# for iGroup in range(nb_groups):
# cmd = cmd + ' ' + file_dwi + '_mean_' + str(iGroup)
# Average DW Images
# TODO: USEFULL ???
sct.printv('\nAveraging all DW images...', param.verbose)
fname_dwi_mean = file_dwi+'_mean'
sct.run('sct_maths -i '+file_dwi_group+ext_data+' -o '+file_dwi_group+'_mean'+ext_data+' -mean t', param.verbose)
# segment dwi images using otsu algorithm
if param.otsu:
sct.printv('\nSegment group DWI using OTSU algorithm...', param.verbose)
# import module
otsu = importlib.import_module('sct_otsu')
# get class from module
param_otsu = otsu.param() #getattr(otsu, param)
param_otsu.fname_data = file_dwi_group+ext_data
param_otsu.threshold = param.otsu
param_otsu.file_suffix = '_seg'
# run otsu
otsu.otsu(param_otsu)
file_dwi_group = file_dwi_group+'_seg'
# extract first DWI volume as target for registration
nii = Image(file_dwi_group+ext_data)
data_crop = nii.data[:, :, :, index_dwi[0]:index_dwi[0]+1]
nii.data = data_crop
target_dwi_name = 'target_dwi'
nii.setFileName(target_dwi_name+ext_data)
nii.save()
# START MOCO
#===================================================================================================================
# Estimate moco on b0 groups
sct.printv('\n-------------------------------------------------------------------------------', param.verbose)
sct.printv(' Estimating motion on b=0 images...', param.verbose)
sct.printv('-------------------------------------------------------------------------------', param.verbose)
param_moco = param
param_moco.file_data = 'b0'
if index_dwi[0] != 0:
# If first DWI is not the first volume (most common), then there is a least one b=0 image before. In that case
# select it as the target image for registration of all b=0
param_moco.file_target = file_data + '_T' + str(index_b0[index_dwi[0]-1]).zfill(4)
else:
# If first DWI is the first volume, then the target b=0 is the first b=0 from the index_b0.
param_moco.file_target = file_data + '_T' + str(index_b0[0]).zfill(4)
param_moco.path_out = ''
param_moco.todo = 'estimate'
param_moco.mat_moco = 'mat_b0groups'
moco.moco(param_moco)
# Estimate moco on dwi groups
sct.printv('\n-------------------------------------------------------------------------------', param.verbose)
sct.printv(' Estimating motion on DW images...', param.verbose)
sct.printv('-------------------------------------------------------------------------------', param.verbose)
param_moco.file_data = file_dwi_group
param_moco.file_target = target_dwi_name # target is the first DW image (closest to the first b=0)
param_moco.path_out = ''
# param_moco.todo = 'estimate'
param_moco.todo = 'estimate_and_apply'
param_moco.mat_moco = 'mat_dwigroups'
moco.moco(param_moco)
# create final mat folder
sct.create_folder(mat_final)
# Copy b=0 registration matrices
sct.printv('\nCopy b=0 registration matrices...', param.verbose)
for it in range(nb_b0):
sct.run('cp '+'mat_b0groups/'+'mat.T'+str(it)+ext_mat+' '+mat_final+'mat.T'+str(index_b0[it])+ext_mat, param.verbose)
# Copy DWI registration matrices
sct.printv('\nCopy DWI registration matrices...', param.verbose)
for iGroup in range(nb_groups):
for dwi in range(len(group_indexes[iGroup])):
sct.run('cp '+'mat_dwigroups/'+'mat.T'+str(iGroup)+ext_mat+' '+mat_final+'mat.T'+str(group_indexes[iGroup][dwi])+ext_mat, param.verbose)
# Spline Regularization along T
if param.spline_fitting:
moco.spline(mat_final, nt, nz, param.verbose, np.array(index_b0), param.plot_graph)
# combine Eddy Matrices
if param.run_eddy:
param.mat_2_combine = 'mat_eddy'
param.mat_final = mat_final
moco.combine_matrix(param)
# Apply moco on all dmri data
sct.printv('\n-------------------------------------------------------------------------------', param.verbose)
sct.printv(' Apply moco', param.verbose)
sct.printv('-------------------------------------------------------------------------------', param.verbose)
param_moco.file_data = file_data
param_moco.file_target = file_dwi+'_mean_'+str(0) # reference for reslicing into proper coordinate system
param_moco.path_out = ''
param_moco.mat_moco = mat_final
param_moco.todo = 'apply'
moco.moco(param_moco)
# copy geometric information from header
# NB: this is required because WarpImageMultiTransform in 2D mode wrongly sets pixdim(3) to "1".
im_dmri = Image(file_data+ext_data)
im_dmri_moco = Image(file_data+param.suffix+ext_data)
im_dmri_moco = copy_header(im_dmri, im_dmri_moco)
im_dmri_moco.save()
# generate b0_moco_mean and dwi_moco_mean
cmd = 'sct_dmri_separate_b0_and_dwi -i '+file_data+param.suffix+ext_data+' -bvec bvecs.txt -a 1'
if not param.fname_bvals == '':
cmd = cmd+' -m '+param.fname_bvals
sct.run(cmd, param.verbose)
def apply(self):
# Initialization
fname_src = self.input_filename # source image (moving)
fname_warp_list = self.warp_input # list of warping fields
fname_out = self.output_filename # output
fname_dest = self.fname_dest # destination image (fix)
verbose = self.verbose
remove_temp_files = self.remove_temp_files
crop_reference = self.crop # if = 1, put 0 everywhere around warping field, if = 2, real crop
interp = sct.get_interpolation('isct_antsApplyTransforms', self.interp)
# Parse list of warping fields
sct.printv('\nParse list of warping fields...', verbose)
use_inverse = []
fname_warp_list_invert = []
# fname_warp_list = fname_warp_list.replace(' ', '') # remove spaces
# fname_warp_list = fname_warp_list.split(",") # parse with comma
for i in range(len(fname_warp_list)):
# Check if inverse matrix is specified with '-' at the beginning of file name
if fname_warp_list[i].find('-') == 0:
use_inverse.append('-i ')
fname_warp_list[i] = fname_warp_list[i][1:] # remove '-'
else:
use_inverse.append('')
sct.printv(
' Transfo #' + str(i) + ': ' + use_inverse[i] +
fname_warp_list[i], verbose)
fname_warp_list_invert.append(use_inverse[i] + fname_warp_list[i])
# need to check if last warping field is an affine transfo
isLastAffine = False
path_fname, file_fname, ext_fname = sct.extract_fname(
fname_warp_list_invert[-1])
if ext_fname in ['.txt', '.mat']:
isLastAffine = True
# check if destination file is 3d
if not sct.check_if_3d(fname_dest):
sct.printv('ERROR: Destination data must be 3d')
# N.B. Here we take the inverse of the warp list, because sct_WarpImageMultiTransform concatenates in the reverse order
fname_warp_list_invert.reverse()
# Extract path, file and extension
path_src, file_src, ext_src = sct.extract_fname(fname_src)
path_dest, file_dest, ext_dest = sct.extract_fname(fname_dest)
# Get output folder and file name
if fname_out == '':
path_out = '' # output in user's current directory
file_out = file_src + '_reg'
ext_out = ext_src
fname_out = path_out + file_out + ext_out
# Get dimensions of data
sct.printv('\nGet dimensions of data...', verbose)
from msct_image import Image
nx, ny, nz, nt, px, py, pz, pt = Image(fname_src).dim
# nx, ny, nz, nt, px, py, pz, pt = sct.get_dimension(fname_src)
sct.printv(
' ' + str(nx) + ' x ' + str(ny) + ' x ' + str(nz) + ' x ' +
str(nt), verbose)
# if 3d
if nt == 1:
# Apply transformation
sct.printv('\nApply transformation...', verbose)
if nz in [0, 1]:
dim = '2'
else:
dim = '3'
sct.run(
'isct_antsApplyTransforms -d ' + dim + ' -i ' + fname_src +
' -o ' + fname_out + ' -t ' +
' '.join(fname_warp_list_invert) + ' -r ' + fname_dest +
interp, verbose)
# if 4d, loop across the T dimension
else:
# create temporary folder
sct.printv('\nCreate temporary folder...', verbose)
path_tmp = sct.slash_at_the_end(
'tmp.' + time.strftime("%y%m%d%H%M%S"), 1)
# sct.run('mkdir '+path_tmp, verbose)
sct.run('mkdir ' + path_tmp, verbose)
# convert to nifti into temp folder
sct.printv(
'\nCopying input data to tmp folder and convert to nii...',
verbose)
from sct_convert import convert
convert(fname_src, path_tmp + 'data.nii', squeeze_data=False)
sct.run('cp ' + fname_dest + ' ' + path_tmp + file_dest + ext_dest)
fname_warp_list_tmp = []
for fname_warp in fname_warp_list:
path_warp, file_warp, ext_warp = sct.extract_fname(fname_warp)
sct.run('cp ' + fname_warp + ' ' + path_tmp + file_warp +
ext_warp)
fname_warp_list_tmp.append(file_warp + ext_warp)
fname_warp_list_invert_tmp = fname_warp_list_tmp[::-1]
os.chdir(path_tmp)
# split along T dimension
sct.printv('\nSplit along T dimension...', verbose)
from sct_image import split_data
im_dat = Image('data.nii')
im_header = im_dat.hdr
data_split_list = split_data(im_dat, 3)
for im in data_split_list:
im.save()
# apply transfo
sct.printv('\nApply transformation to each 3D volume...', verbose)
for it in range(nt):
file_data_split = 'data_T' + str(it).zfill(4) + '.nii'
file_data_split_reg = 'data_reg_T' + str(it).zfill(4) + '.nii'
status, output = sct.run(
'isct_antsApplyTransforms -d 3 -i ' + file_data_split +
' -o ' + file_data_split_reg + ' -t ' +
' '.join(fname_warp_list_invert_tmp) + ' -r ' + file_dest +
ext_dest + interp, verbose)
# Merge files back
sct.printv('\nMerge file back...', verbose)
from sct_image import concat_data
import glob
path_out, name_out, ext_out = sct.extract_fname(fname_out)
# im_list = [Image(file_name) for file_name in glob.glob('data_reg_T*.nii')]
# concat_data use to take a list of image in input, now takes a list of file names to open the files one by one (see issue #715)
fname_list = glob.glob('data_reg_T*.nii')
im_out = concat_data(fname_list, 3, im_header['pixdim'])
im_out.setFileName(name_out + ext_out)
im_out.save(squeeze_data=False)
os.chdir('..')
sct.generate_output_file(path_tmp + name_out + ext_out, fname_out)
# Delete temporary folder if specified
if int(remove_temp_files):
sct.printv('\nRemove temporary files...', verbose)
sct.run('rm -rf ' + path_tmp, verbose, error_exit='warning')
# 2. crop the resulting image using dimensions from the warping field
warping_field = fname_warp_list_invert[-1]
# if last warping field is an affine transfo, we need to compute the space of the concatenate warping field:
if isLastAffine:
sct.printv(
'WARNING: the resulting image could have wrong apparent results. You should use an affine transformation as last transformation...',
verbose, 'warning')
elif crop_reference == 1:
ImageCropper(input_file=fname_out,
output_file=fname_out,
ref=warping_field,
background=0).crop()
# sct.run('sct_crop_image -i '+fname_out+' -o '+fname_out+' -ref '+warping_field+' -b 0')
elif crop_reference == 2:
ImageCropper(input_file=fname_out,
output_file=fname_out,
ref=warping_field).crop()
# sct.run('sct_crop_image -i '+fname_out+' -o '+fname_out+' -ref '+warping_field)
# display elapsed time
sct.printv('\nDone! To view results, type:', verbose)
sct.printv('fslview ' + fname_dest + ' ' + fname_out + ' &\n', verbose,
'info')
def register_images(fname_source, fname_dest, mask='', paramreg=Paramreg(step='0', type='im', algo='Translation', metric='MI', iter='5', shrink='1', smooth='0', gradStep='0.5'),
ants_registration_params={'rigid': '', 'affine': '', 'compositeaffine': '', 'similarity': '', 'translation': '','bspline': ',10', 'gaussiandisplacementfield': ',3,0',
'bsplinedisplacementfield': ',5,10', 'syn': ',3,0', 'bsplinesyn': ',1,3'}, remove_tmp_folder=1, verbose=0):
"""Slice-by-slice registration of two images.
We first split the 3D images into 2D images (and the mask if inputted). Then we register slices of the two images
that physically correspond to one another looking at the physical origin of each image. The images can be of
different sizes but the destination image must be smaller thant the input image. We do that using antsRegistration
in 2D. Once this has been done for each slices, we gather the results and return them.
Algorithms implemented: translation, rigid, affine, syn and BsplineSyn.
N.B.: If the mask is inputted, it must also be 3D and it must be in the same space as the destination image.
input:
fname_source: name of moving image (type: string)
fname_dest: name of fixed image (type: string)
mask[optional]: name of mask file (type: string) (parameter -x of antsRegistration)
paramreg[optional]: parameters of antsRegistration (type: Paramreg class from sct_register_multimodal)
ants_registration_params[optional]: specific algorithm's parameters for antsRegistration (type: dictionary)
output:
if algo==translation:
x_displacement: list of translation along x axis for each slice (type: list)
y_displacement: list of translation along y axis for each slice (type: list)
if algo==rigid:
x_displacement: list of translation along x axis for each slice (type: list)
y_displacement: list of translation along y axis for each slice (type: list)
theta_rotation: list of rotation angle in radian (and in ITK's coordinate system) for each slice (type: list)
if algo==affine or algo==syn or algo==bsplinesyn:
creation of two 3D warping fields (forward and inverse) that are the concatenations of the slice-by-slice
warps.
"""
# Extracting names
path_i, root_i, ext_i = sct.extract_fname(fname_source)
path_d, root_d, ext_d = sct.extract_fname(fname_dest)
# set metricSize
if paramreg.metric == 'MI':
metricSize = '32' # corresponds to number of bins
else:
metricSize = '4' # corresponds to radius (for CC, MeanSquares...)
# Get image dimensions and retrieve nz
print '\nGet image dimensions of destination image...'
nx, ny, nz, nt, px, py, pz, pt = Image(fname_dest).dim
print '.. matrix size: '+str(nx)+' x '+str(ny)+' x '+str(nz)
print '.. voxel size: '+str(px)+'mm x '+str(py)+'mm x '+str(pz)+'mm'
# Define x and y displacement as list
x_displacement = [0 for i in range(nz)]
y_displacement = [0 for i in range(nz)]
theta_rotation = [0 for i in range(nz)]
# create temporary folder
print('\nCreate temporary folder...')
path_tmp = 'tmp.'+time.strftime("%y%m%d%H%M%S")
sct.create_folder(path_tmp)
print '\nCopy input data...'
sct.run('cp '+fname_source+ ' ' + path_tmp +'/'+ root_i+ext_i)
sct.run('cp '+fname_dest+ ' ' + path_tmp +'/'+ root_d+ext_d)
if mask:
sct.run('cp '+mask+ ' '+path_tmp +'/mask.nii.gz')
# go to temporary folder
os.chdir(path_tmp)
# Split input volume along z
print '\nSplit input volume...'
from sct_image import split_data
im_source = Image(fname_source)
split_source_list = split_data(im_source, 2)
for im_src in split_source_list:
im_src.save()
# Split destination volume along z
print '\nSplit destination volume...'
im_dest = Image(fname_dest)
split_dest_list = split_data(im_dest, 2)
for im_d in split_dest_list:
im_d.save()
# Split mask volume along z
if mask:
print '\nSplit mask volume...'
im_mask = Image('mask.nii.gz')
split_mask_list = split_data(im_mask, 2)
for im_m in split_mask_list:
im_m.save()
coord_origin_dest = im_dest.transfo_pix2phys([[0,0,0]])
coord_origin_input = im_source.transfo_pix2phys([[0,0,0]])
coord_diff_origin = (asarray(coord_origin_dest[0]) - asarray(coord_origin_input[0])).tolist()
[x_o, y_o, z_o] = [coord_diff_origin[0] * 1.0/px, coord_diff_origin[1] * 1.0/py, coord_diff_origin[2] * 1.0/pz]
if paramreg.algo == 'BSplineSyN' or paramreg.algo == 'SyN' or paramreg.algo == 'Affine':
list_warp_x = []
list_warp_x_inv = []
list_warp_y = []
list_warp_y_inv = []
name_warp_final = 'Warp_total' #if modified, name should also be modified in msct_register (algo slicereg2d_bsplinesyn and slicereg2d_syn)
# loop across slices
for i in range(nz):
# set masking
num = numerotation(i)
num_2 = numerotation(int(num) + int(z_o))
if mask:
masking = '-x mask_Z' +num+ '.nii'
else:
masking = ''
cmd = ('isct_antsRegistration '
'--dimensionality 2 '
'--transform '+paramreg.algo+'['+str(paramreg.gradStep) +
ants_registration_params[paramreg.algo.lower()]+'] '
'--metric '+paramreg.metric+'['+root_d+'_Z'+ num +'.nii' +','+root_i+'_Z'+ num_2 +'.nii' +',1,'+metricSize+'] ' #[fixedImage,movingImage,metricWeight +nb_of_bins (MI) or radius (other)
'--convergence '+str(paramreg.iter)+' '
'--shrink-factors '+str(paramreg.shrink)+' '
'--smoothing-sigmas '+str(paramreg.smooth)+'mm '
#'--restrict-deformation 1x1x0 ' # how to restrict? should not restrict here, if transform is precised...?
'--output [transform_' + num + ','+root_i+'_Z'+ num_2 +'reg.nii] ' #--> file.mat (contains Tx,Ty, theta)
'--interpolation BSpline[3] '
+masking)
try:
sct.run(cmd)
if paramreg.algo == 'Rigid' or paramreg.algo == 'Translation':
f = 'transform_' +num+ '0GenericAffine.mat'
matfile = loadmat(f, struct_as_record=True)
array_transfo = matfile['AffineTransform_double_2_2']
x_displacement[i] = array_transfo[4][0] # Tx in ITK'S coordinate system
y_displacement[i] = array_transfo[5][0] # Ty in ITK'S and fslview's coordinate systems
theta_rotation[i] = asin(array_transfo[2]) # angle of rotation theta in ITK'S coordinate system (minus theta for fslview)
if paramreg.algo == 'Affine':
# New process added for generating total nifti warping field from mat warp
name_dest = root_d+'_Z'+ num +'.nii'
name_reg = root_i+'_Z'+ num +'reg.nii'
name_output_warp = 'warp_from_mat_' + num_2 + '.nii.gz'
name_output_warp_inverse = 'warp_from_mat_' + num + '_inverse.nii.gz'
name_warp_null = 'warp_null_' + num + '.nii.gz'
name_warp_null_dest = 'warp_null_dest' + num + '.nii.gz'
name_warp_mat = 'transform_' + num + '0GenericAffine.mat'
# Generating null nifti warping fields
nx, ny, nz, nt, px, py, pz, pt = Image(name_reg).dim
nx_d, ny_d, nz_d, nt_d, px_d, py_d, pz_d, pt_d = Image(name_dest).dim
x_trans = [0 for i in range(nz)]
x_trans_d = [0 for i in range(nz_d)]
y_trans= [0 for i in range(nz)]
y_trans_d = [0 for i in range(nz_d)]
generate_warping_field(name_reg, x_trans=x_trans, y_trans=y_trans, fname=name_warp_null, verbose=0)
generate_warping_field(name_dest, x_trans=x_trans_d, y_trans=y_trans_d, fname=name_warp_null_dest, verbose=0)
# Concatenating mat wrp and null nifti warp to obtain equivalent nifti warp to mat warp
sct.run('isct_ComposeMultiTransform 2 ' + name_output_warp + ' -R ' + name_reg + ' ' + name_warp_null + ' ' + name_warp_mat)
sct.run('isct_ComposeMultiTransform 2 ' + name_output_warp_inverse + ' -R ' + name_dest + ' ' + name_warp_null_dest + ' -i ' + name_warp_mat)
# Split the warping fields into two for displacement along x and y before merge
sct.run('sct_image -i ' + name_output_warp + ' -mcs -o transform_'+num+'0Warp.nii.gz')
sct.run('sct_image -i ' + name_output_warp_inverse + ' -mcs -o transform_'+num+'0InverseWarp.nii.gz')
# List names of warping fields for futur merge
list_warp_x.append('transform_'+num+'0Warp_x.nii.gz')
list_warp_x_inv.append('transform_'+num+'0InverseWarp_x.nii.gz')
list_warp_y.append('transform_'+num+'0Warp_y.nii.gz')
list_warp_y_inv.append('transform_'+num+'0InverseWarp_y.nii.gz')
if paramreg.algo == 'BSplineSyN' or paramreg.algo == 'SyN':
# Split the warping fields into two for displacement along x and y before merge
# Need to separate the merge for x and y displacement as merge of 3d warping fields does not work properly
sct.run('sct_image -i transform_'+num+'0Warp.nii.gz -mcs -o transform_'+num+'0Warp.nii.gz')
sct.run('sct_image -i transform_'+num+'0InverseWarp.nii.gz -mcs -o transform_'+num+'0InverseWarp.nii.gz')
# List names of warping fields for futur merge
list_warp_x.append('transform_'+num+'0Warp_x.nii.gz')
list_warp_x_inv.append('transform_'+num+'0InverseWarp_x.nii.gz')
list_warp_y.append('transform_'+num+'0Warp_y.nii.gz')
list_warp_y_inv.append('transform_'+num+'0InverseWarp_y.nii.gz')
# if an exception occurs with ants, take the last value for the transformation
except Exception, e:
sct.printv('WARNING, an error occurred: '+str(e)+'\n', verbose, 'warning')
if paramreg.algo == 'Rigid' or paramreg.algo == 'Translation':
x_displacement[i] = x_displacement[i-1]
y_displacement[i] = y_displacement[i-1]
theta_rotation[i] = theta_rotation[i-1]
if paramreg.algo == 'BSplineSyN' or paramreg.algo == 'SyN' or paramreg.algo == 'Affine':
print'Problem with ants for slice '+str(i)+'. Copy of the last warping field.'
sct.run('cp transform_' + numerotation(i-1) + '0Warp.nii.gz transform_' + num + '0Warp.nii.gz')
sct.run('cp transform_' + numerotation(i-1) + '0InverseWarp.nii.gz transform_' + num + '0InverseWarp.nii.gz')
# Split the warping fields into two for displacement along x and y before merge
# sct.run('isct_c3d -mcs transform_'+num+'0Warp.nii.gz -oo transform_'+num+'0Warp_x.nii.gz transform_'+num+'0Warp_y.nii.gz')
# sct.run('isct_c3d -mcs transform_'+num+'0InverseWarp.nii.gz -oo transform_'+num+'0InverseWarp_x.nii.gz transform_'+num+'0InverseWarp_y.nii.gz')
sct.run('sct_image -i transform_'+num+'0Warp.nii.gz -mcs -o transform_'+num+'0Warp.nii.gz')
sct.run('sct_image -i transform_'+num+'0InverseWarp.nii.gz -mcs -o transform_'+num+'0InverseWarp.nii.gz')
# List names of warping fields for futur merge
list_warp_x.append('transform_'+num+'0Warp_x.nii.gz')
list_warp_x_inv.append('transform_'+num+'0InverseWarp_x.nii.gz')
list_warp_y.append('transform_'+num+'0Warp_y.nii.gz')
list_warp_y_inv.append('transform_'+num+'0InverseWarp_y.nii.gz')
def find_centerline(algo, image_fname, path_sct, contrast_type, brain_bool,
folder_output, remove_temp_files, centerline_fname):
if Image(image_fname).dim[2] == 1: # isct_spine_detect requires nz > 1
from sct_image import concat_data
im_concat = concat_data([image_fname, image_fname], dim=2)
im_concat.save(sct.add_suffix(image_fname, '_concat'))
image_fname = sct.add_suffix(image_fname, '_concat')
bool_2d = True
else:
bool_2d = False
if algo == 'svm':
# run optic on a heatmap computed by a trained SVM+HoG algorithm
optic_models_fname = os.path.join(path_sct, 'data', 'optic_models',
'{}_model'.format(contrast_type))
_, centerline_filename = optic.detect_centerline(
image_fname=image_fname,
contrast_type=contrast_type,
optic_models_path=optic_models_fname,
folder_output=folder_output,
remove_temp_files=remove_temp_files,
output_roi=False,
verbose=0)
elif algo == 'cnn':
# CNN parameters
dct_patch_ctr = {
't2': {
'size': (80, 80),
'mean': 51.1417,
'std': 57.4408
},
't2s': {
'size': (80, 80),
'mean': 68.8591,
'std': 71.4659
},
't1': {
'size': (80, 80),
'mean': 55.7359,
'std': 64.3149
},
'dwi': {
'size': (80, 80),
'mean': 55.744,
'std': 45.003
}
}
dct_params_ctr = {
't2': {
'features': 16,
'dilation_layers': 2
},
't2s': {
'features': 8,
'dilation_layers': 3
},
't1': {
'features': 24,
'dilation_layers': 3
},
'dwi': {
'features': 8,
'dilation_layers': 2
}
}
# load model
ctr_model_fname = os.path.join(path_sct, 'data', 'deepseg_sc_models',
'{}_ctr.h5'.format(contrast_type))
ctr_model = nn_architecture_ctr(
height=dct_patch_ctr[contrast_type]['size'][0],
width=dct_patch_ctr[contrast_type]['size'][1],
channels=1,
classes=1,
features=dct_params_ctr[contrast_type]['features'],
depth=2,
temperature=1.0,
padding='same',
batchnorm=True,
dropout=0.0,
dilation_layers=dct_params_ctr[contrast_type]['dilation_layers'])
ctr_model.load_weights(ctr_model_fname)
sct.log.info("Resample the image to 0.5 mm isotropic resolution...")
fname_res = sct.add_suffix(image_fname, '_resampled')
input_resolution = Image(image_fname).dim[4:7]
new_resolution = 'x'.join(['0.5', '0.5', str(input_resolution[2])])
spinalcordtoolbox.resample.nipy_resample.resample_file(image_fname,
fname_res,
new_resolution,
'mm',
'linear',
verbose=0)
# compute the heatmap
fname_heatmap = sct.add_suffix(image_fname, "_heatmap")
img_filename = ''.join(sct.extract_fname(fname_heatmap)[:2])
fname_heatmap_nii = img_filename + '.nii'
z_max = heatmap(filename_in=fname_res,
filename_out=fname_heatmap_nii,
model=ctr_model,
patch_shape=dct_patch_ctr[contrast_type]['size'],
mean_train=dct_patch_ctr[contrast_type]['mean'],
std_train=dct_patch_ctr[contrast_type]['std'],
brain_bool=brain_bool)
# run optic on the heatmap
centerline_filename = sct.add_suffix(fname_heatmap, "_ctr")
heatmap2optic(fname_heatmap=fname_heatmap_nii,
lambda_value=7 if contrast_type == 't2s' else 1,
fname_out=centerline_filename,
z_max=z_max if brain_bool else None)
elif algo == 'viewer':
centerline_filename = sct.add_suffix(image_fname, "_ctr")
fname_labels_viewer = _call_viewer_centerline(fname_in=image_fname)
centerline_filename = extract_centerline(fname_labels_viewer,
remove_temp_files=True,
algo_fitting='nurbs',
nurbs_pts_number=8000)
elif algo == 'manual':
centerline_filename = sct.add_suffix(image_fname, "_ctr")
image_manual_centerline = Image(centerline_fname)
# Re-orient and Re-sample the manual centerline
msct_image.change_orientation(image_manual_centerline,
'RPI').save(centerline_filename)
else:
sct.log.error(
'The parameter "-centerline" is incorrect. Please try again.')
sys.exit(1)
if algo != 'cnn':
sct.log.info("Resample the image to 0.5 mm isotropic resolution...")
fname_res = sct.add_suffix(image_fname, '_resampled')
input_resolution = Image(image_fname).dim[4:7]
new_resolution = 'x'.join(['0.5', '0.5', str(input_resolution[2])])
spinalcordtoolbox.resample.nipy_resample.resample_file(image_fname,
fname_res,
new_resolution,
'mm',
'linear',
verbose=0)
spinalcordtoolbox.resample.nipy_resample.resample_file(
centerline_filename,
centerline_filename,
new_resolution,
'mm',
'linear',
verbose=0)
if bool_2d:
from sct_image import split_data
im_split_lst = split_data(Image(centerline_filename), dim=2)
im_split_lst[0].save(centerline_filename)
return fname_res, centerline_filename
def main(fname_anat, fname_centerline, degree_poly, centerline_fitting, interp,
remove_temp_files, verbose):
# extract path of the script
path_script = os.path.dirname(__file__) + '/'
# Parameters for debug mode
if param.debug == 1:
print '\n*** WARNING: DEBUG MODE ON ***\n'
status, path_sct_data = commands.getstatusoutput(
'echo $SCT_TESTING_DATA_DIR')
fname_anat = path_sct_data + '/t2/t2.nii.gz'
fname_centerline = path_sct_data + '/t2/t2_seg.nii.gz'
# extract path/file/extension
path_anat, file_anat, ext_anat = sct.extract_fname(fname_anat)
# Display arguments
print '\nCheck input arguments...'
print ' Input volume ...................... ' + fname_anat
print ' Centerline ........................ ' + fname_centerline
print ''
# Get input image orientation
im_anat = Image(fname_anat)
input_image_orientation = get_orientation_3d(im_anat)
# Reorient input data into RL PA IS orientation
im_centerline = Image(fname_centerline)
im_anat_orient = set_orientation(im_anat, 'RPI')
im_anat_orient.setFileName('tmp.anat_orient.nii')
im_centerline_orient = set_orientation(im_centerline, 'RPI')
im_centerline_orient.setFileName('tmp.centerline_orient.nii')
# Open centerline
#==========================================================================================
print '\nGet dimensions of input centerline...'
nx, ny, nz, nt, px, py, pz, pt = im_centerline_orient.dim
print '.. matrix size: ' + str(nx) + ' x ' + str(ny) + ' x ' + str(nz)
print '.. voxel size: ' + str(px) + 'mm x ' + str(py) + 'mm x ' + str(
pz) + 'mm'
print '\nOpen centerline volume...'
data = im_centerline_orient.data
X, Y, Z = (data > 0).nonzero()
min_z_index, max_z_index = min(Z), max(Z)
# loop across z and associate x,y coordinate with the point having maximum intensity
x_centerline = [0 for iz in range(min_z_index, max_z_index + 1, 1)]
y_centerline = [0 for iz in range(min_z_index, max_z_index + 1, 1)]
z_centerline = [iz for iz in range(min_z_index, max_z_index + 1, 1)]
# Two possible scenario:
# 1. the centerline is probabilistic: each slices contains voxels with the probability of containing the centerline [0:...:1]
# We only take the maximum value of the image to aproximate the centerline.
# 2. The centerline/segmentation image contains many pixels per slice with values {0,1}.
# We take all the points and approximate the centerline on all these points.
X, Y, Z = ((data < 1) * (data > 0)).nonzero() # X is empty if binary image
if (len(X) > 0): # Scenario 1
for iz in range(min_z_index, max_z_index + 1, 1):
x_centerline[iz - min_z_index], y_centerline[
iz - min_z_index] = numpy.unravel_index(
data[:, :, iz].argmax(), data[:, :, iz].shape)
else: # Scenario 2
for iz in range(min_z_index, max_z_index + 1, 1):
x_seg, y_seg = (data[:, :, iz] > 0).nonzero()
if len(x_seg) > 0:
x_centerline[iz - min_z_index] = numpy.mean(x_seg)
y_centerline[iz - min_z_index] = numpy.mean(y_seg)
# TODO: find a way to do the previous loop with this, which is more neat:
# [numpy.unravel_index(data[:,:,iz].argmax(), data[:,:,iz].shape) for iz in range(0,nz,1)]
# clear variable
del data
# Fit the centerline points with the kind of curve given as argument of the script and return the new smoothed coordinates
if centerline_fitting == 'nurbs':
try:
x_centerline_fit, y_centerline_fit = b_spline_centerline(
x_centerline, y_centerline, z_centerline)
except ValueError:
print "splines fitting doesn't work, trying with polynomial fitting...\n"
x_centerline_fit, y_centerline_fit = polynome_centerline(
x_centerline, y_centerline, z_centerline)
elif centerline_fitting == 'polynome':
x_centerline_fit, y_centerline_fit = polynome_centerline(
x_centerline, y_centerline, z_centerline)
#==========================================================================================
# Split input volume
print '\nSplit input volume...'
im_anat_orient_split_list = split_data(im_anat_orient, 2)
file_anat_split = []
for im in im_anat_orient_split_list:
file_anat_split.append(im.absolutepath)
im.save()
# initialize variables
file_mat_inv_cumul = [
'tmp.mat_inv_cumul_Z' + str(z).zfill(4) for z in range(0, nz, 1)
]
z_init = min_z_index
displacement_max_z_index = x_centerline_fit[
z_init - min_z_index] - x_centerline_fit[max_z_index - min_z_index]
# write centerline as text file
print '\nGenerate fitted transformation matrices...'
file_mat_inv_cumul_fit = [
'tmp.mat_inv_cumul_fit_Z' + str(z).zfill(4) for z in range(0, nz, 1)
]
for iz in range(min_z_index, max_z_index + 1, 1):
# compute inverse cumulative fitted transformation matrix
fid = open(file_mat_inv_cumul_fit[iz], 'w')
if (x_centerline[iz - min_z_index] == 0
and y_centerline[iz - min_z_index] == 0):
displacement = 0
else:
displacement = x_centerline_fit[
z_init - min_z_index] - x_centerline_fit[iz - min_z_index]
fid.write('%i %i %i %f\n' % (1, 0, 0, displacement))
fid.write('%i %i %i %f\n' % (0, 1, 0, 0))
fid.write('%i %i %i %i\n' % (0, 0, 1, 0))
fid.write('%i %i %i %i\n' % (0, 0, 0, 1))
fid.close()
# we complete the displacement matrix in z direction
for iz in range(0, min_z_index, 1):
fid = open(file_mat_inv_cumul_fit[iz], 'w')
fid.write('%i %i %i %f\n' % (1, 0, 0, 0))
fid.write('%i %i %i %f\n' % (0, 1, 0, 0))
fid.write('%i %i %i %i\n' % (0, 0, 1, 0))
fid.write('%i %i %i %i\n' % (0, 0, 0, 1))
fid.close()
for iz in range(max_z_index + 1, nz, 1):
fid = open(file_mat_inv_cumul_fit[iz], 'w')
fid.write('%i %i %i %f\n' % (1, 0, 0, displacement_max_z_index))
fid.write('%i %i %i %f\n' % (0, 1, 0, 0))
fid.write('%i %i %i %i\n' % (0, 0, 1, 0))
fid.write('%i %i %i %i\n' % (0, 0, 0, 1))
fid.close()
# apply transformations to data
print '\nApply fitted transformation matrices...'
file_anat_split_fit = [
'tmp.anat_orient_fit_Z' + str(z).zfill(4) for z in range(0, nz, 1)
]
for iz in range(0, nz, 1):
# forward cumulative transformation to data
sct.run(fsloutput + 'flirt -in ' + file_anat_split[iz] + ' -ref ' +
file_anat_split[iz] + ' -applyxfm -init ' +
file_mat_inv_cumul_fit[iz] + ' -out ' +
file_anat_split_fit[iz] + ' -interp ' + interp)
# Merge into 4D volume
print '\nMerge into 4D volume...'
from glob import glob
im_to_concat_list = [
Image(fname) for fname in glob('tmp.anat_orient_fit_Z*.nii')
]
im_concat_out = concat_data(im_to_concat_list, 2)
im_concat_out.setFileName('tmp.anat_orient_fit.nii')
im_concat_out.save()
# sct.run(fsloutput+'fslmerge -z tmp.anat_orient_fit tmp.anat_orient_fit_z*')
# Reorient data as it was before
print '\nReorient data back into native orientation...'
fname_anat_fit_orient = set_orientation(im_concat_out.absolutepath,
input_image_orientation,
filename=True)
move(fname_anat_fit_orient, 'tmp.anat_orient_fit_reorient.nii')
# Generate output file (in current folder)
print '\nGenerate output file (in current folder)...'
sct.generate_output_file('tmp.anat_orient_fit_reorient.nii',
file_anat + '_flatten' + ext_anat)
# Delete temporary files
if remove_temp_files == 1:
print '\nDelete temporary files...'
sct.run('rm -rf tmp.*')
# to view results
print '\nDone! To view results, type:'
print 'fslview ' + file_anat + ext_anat + ' ' + file_anat + '_flatten' + ext_anat + ' &\n'
def register2d_centermassrot(fname_src, fname_dest, fname_warp='warp_forward.nii.gz', fname_warp_inv='warp_inverse.nii.gz', rot=1, polydeg=0, path_qc='./', verbose=0, pca_eigenratio_th=1.6):
"""
Rotate the source image to match the orientation of the destination image, using the first and second eigenvector
of the PCA. This function should be used on segmentations (not images).
This works for 2D and 3D images. If 3D, it splits the image and performs the rotation slice-by-slice.
input:
fname_source: name of moving image (type: string), if rot == 2, this needs to be a list with the first element
being the image fname and the second the segmentation fname
fname_dest: name of fixed image (type: string), if rot == 2, needs to be a list
fname_warp: name of output 3d forward warping field
fname_warp_inv: name of output 3d inverse warping field
rot: estimate rotation with pca (=1), hog (=2) or no rotation (=0) Default = 1
Depending on the rotation method, input might be segmentation only or image and segmentation
polydeg: degree of polynomial regularization along z for rotation angle (type: int). 0: no regularization
verbose:
output:
none
"""
if rot == 2: # if following methods need im and seg, add "and rot == x"
fname_src_im = fname_src[0]
fname_dest_im = fname_dest[0]
fname_src_seg = fname_src[1]
fname_dest_seg = fname_dest[1]
del fname_src
del fname_dest # to be sure it is not missused later
if verbose == 2:
import matplotlib
matplotlib.use('Agg') # prevent display figure
import matplotlib.pyplot as plt
# Get image dimensions and retrieve nz
sct.printv('\nGet image dimensions of destination image...', verbose)
if rot == 1 or rot == 0:
nx, ny, nz, nt, px, py, pz, pt = Image(fname_dest).dim
else:
nx, ny, nz, nt, px, py, pz, pt = Image(fname_dest_im).dim
sct.printv(' matrix size: ' + str(nx) + ' x ' + str(ny) + ' x ' + str(nz), verbose)
sct.printv(' voxel size: ' + str(px) + 'mm x ' + str(py) + 'mm x ' + str(pz) + 'mm', verbose)
if rot == 1 or rot == 0:
# Split source volume along z
sct.printv('\nSplit input volume...', verbose)
from sct_image import split_data
im_src = Image('src.nii')
split_source_list = split_data(im_src, 2)
for im in split_source_list:
im.save()
# Split destination volume along z
sct.printv('\nSplit destination volume...', verbose)
im_dest = Image('dest.nii')
split_dest_list = split_data(im_dest, 2)
for im in split_dest_list:
im.save()
# display image
data_src = im_src.data
data_dest = im_dest.data
if len(data_src.shape) == 2:
# reshape 2D data into pseudo 3D (only one slice)
new_shape = list(data_src.shape)
new_shape.append(1)
new_shape = tuple(new_shape)
data_src = data_src.reshape(new_shape)
data_dest = data_dest.reshape(new_shape)
elif rot == 2: # im and seg case
# Split source volume along z
sct.printv('\nSplit input volume...', verbose)
from sct_image import split_data
im_src_im = Image('src_im.nii')
split_source_list = split_data(im_src_im, 2)
for im in split_source_list:
im.save()
im_src_seg = Image('src_seg.nii')
split_source_list = split_data(im_src_seg, 2)
for im in split_source_list:
im.save()
# Split destination volume along z
sct.printv('\nSplit destination volume...', verbose)
im_dest_im = Image('dest_im.nii')
split_dest_list = split_data(im_dest_im, 2)
for im in split_dest_list:
im.save()
im_dest_seg = Image('dest_seg.nii')
split_dest_list = split_data(im_dest_seg, 2)
for im in split_dest_list:
im.save()
# display image
data_src_im = im_src_im.data
data_dest_im = im_dest_im.data
data_src_seg = im_src_seg.data
data_dest_seg = im_dest_seg.data
else:
raise ValueError("rot param == " + str(rot) + " not implemented")
# initialize displacement and rotation
coord_src = [None] * nz
pca_src = [None] * nz
coord_dest = [None] * nz
pca_dest = [None] * nz
centermass_src = np.zeros([nz, 2])
centermass_dest = np.zeros([nz, 2])
# displacement_forward = np.zeros([nz, 2])
# displacement_inverse = np.zeros([nz, 2])
angle_src_dest = np.zeros(nz)
z_nonzero = []
if rot == 1 or rot == 0:
# Loop across slices
for iz in range(0, nz):
try:
# compute PCA and get center or mass
coord_src[iz], pca_src[iz], centermass_src[iz, :] = compute_pca(data_src[:, :, iz])
coord_dest[iz], pca_dest[iz], centermass_dest[iz, :] = compute_pca(data_dest[:, :, iz])
# compute (src,dest) angle for first eigenvector
if rot == 1:
eigenv_src = pca_src[iz].components_.T[0][0], pca_src[iz].components_.T[1][0] # pca_src.components_.T[0]
eigenv_dest = pca_dest[iz].components_.T[0][0], pca_dest[iz].components_.T[1][0] # pca_dest.components_.T[0]
# Make sure first element is always positive (to prevent sign flipping)
if eigenv_src[0] <= 0:
eigenv_src = tuple([i * (-1) for i in eigenv_src])
if eigenv_dest[0] <= 0:
eigenv_dest = tuple([i * (-1) for i in eigenv_dest])
angle_src_dest[iz] = angle_between(eigenv_src, eigenv_dest)
# check if ratio between the two eigenvectors is high enough to prevent poor robustness
if pca_src[iz].explained_variance_ratio_[0] / pca_src[iz].explained_variance_ratio_[1] < pca_eigenratio_th:
angle_src_dest[iz] = 0
if pca_dest[iz].explained_variance_ratio_[0] / pca_dest[iz].explained_variance_ratio_[1] < pca_eigenratio_th:
angle_src_dest[iz] = 0
# append to list of z_nonzero
z_nonzero.append(iz)
# if one of the slice is empty, ignore it
except ValueError:
sct.printv('WARNING: Slice #' + str(iz) + ' is empty. It will be ignored.', verbose, 'warning')
elif rot == 2: # im and seg case
raise NotImplementedError("This method is not implemented yet, it will be in a future version")
# for iz in range(0, nz):
# try:
# _, _, centermass_src[iz, :] = compute_pca(data_src_seg[:, :, iz])
# _, _, centermass_dest[iz, :] = compute_pca(data_dest_seg[:, :, iz])
#
# # TODO: Here will be put the new method to find the angle
#
# #angle_src = find_angle(data_src_im[:, :, iz], centermass_src[iz, :], parameters)
# #angle_dest = find_angle(data_dest_im[:, :, iz], centermass_dest[iz, :], parameters)
#
# # if (angle_src is None) or (angle_dest is None):
# # sct.printv('WARNING: Slice #' + str(iz) + ' no angle found in dest or src. It will be ignored.', verbose, 'warning')
# # continue
#
# # angle_src_dest[iz] = angle_src-angle_dest
#
# except ValueError:
# sct.printv('WARNING: Slice #' + str(iz) + ' is empty. It will be ignored.', verbose, 'warning')
else:
raise ValueError("rot param == " + str(rot) + " not implemented")
# regularize rotation
if not polydeg == 0 and (rot == 1 or rot == 2):
coeffs = np.polyfit(z_nonzero, angle_src_dest[z_nonzero], polydeg)
poly = np.poly1d(coeffs)
angle_src_dest_regularized = np.polyval(poly, z_nonzero) # display
if verbose == 2:
plt.plot(180 * angle_src_dest[z_nonzero] / np.pi, 'ob')
plt.plot(180 * angle_src_dest_regularized / np.pi, 'r', linewidth=2)
plt.grid()
plt.xlabel('z')
plt.ylabel('Angle (deg)')
plt.savefig(os.path.join(path_qc, 'register2d_centermassrot_regularize_rotation.png'))
plt.close()
# update variable
angle_src_dest[z_nonzero] = angle_src_dest_regularized
# initialize warping fields
# N.B. forward transfo is defined in destination space and inverse transfo is defined in the source space
if rot == 2:
im_src = im_src_im
im_dest = im_dest_im
data_dest = data_dest_im
data_src = data_src_im
fname_dest = fname_dest_im
fname_src = fname_src_im
# back to original names for the rest of the process
warp_x = np.zeros(data_dest.shape)
warp_y = np.zeros(data_dest.shape)
warp_inv_x = np.zeros(data_src.shape)
warp_inv_y = np.zeros(data_src.shape)
# construct 3D warping matrix
for iz in z_nonzero:
# TODO: replace the thing below with "tqdm-like" logger-based function
# sct.no_new_line_log('{}/{}..'.format(iz + 1, nz))
# get indices of x and y coordinates
row, col = np.indices((nx, ny))
# build 2xn array of coordinates in pixel space
coord_init_pix = np.array([row.ravel(), col.ravel(), np.array(np.ones(len(row.ravel())) * iz)]).T
# convert coordinates to physical space
coord_init_phy = np.array(im_src.transfo_pix2phys(coord_init_pix))
# get centermass coordinates in physical space
centermass_src_phy = im_src.transfo_pix2phys([[centermass_src[iz, :].T[0], centermass_src[iz, :].T[1], iz]])[0]
centermass_dest_phy = im_src.transfo_pix2phys([[centermass_dest[iz, :].T[0], centermass_dest[iz, :].T[1], iz]])[0]
# build rotation matrix
R = np.matrix(((cos(angle_src_dest[iz]), sin(angle_src_dest[iz])), (-sin(angle_src_dest[iz]), cos(angle_src_dest[iz]))))
# build 3D rotation matrix
R3d = np.eye(3)
R3d[0:2, 0:2] = R
# apply forward transformation (in physical space)
coord_forward_phy = np.array(np.dot((coord_init_phy - np.transpose(centermass_dest_phy)), R3d) + np.transpose(centermass_src_phy))
# apply inverse transformation (in physical space)
coord_inverse_phy = np.array(np.dot((coord_init_phy - np.transpose(centermass_src_phy)), R3d.T) + np.transpose(centermass_dest_phy))
# display rotations
if verbose == 2 and not angle_src_dest[iz] == 0:
# compute new coordinates
coord_src_rot = coord_src[iz] * R
coord_dest_rot = coord_dest[iz] * R.T
# generate figure
plt.figure('iz=' + str(iz) + ', angle_src_dest=' + str(angle_src_dest[iz]), figsize=(9, 9))
# plt.ion() # enables interactive mode (allows keyboard interruption)
# plt.title('iz='+str(iz))
for isub in [221, 222, 223, 224]:
# plt.figure
plt.subplot(isub)
# ax = matplotlib.pyplot.axis()
try:
if isub == 221:
plt.scatter(coord_src[iz][:, 0], coord_src[iz][:, 1], s=5, marker='o', zorder=10, color='steelblue',
alpha=0.5)
pcaaxis = pca_src[iz].components_.T
pca_eigenratio = pca_src[iz].explained_variance_ratio_
plt.title('src')
elif isub == 222:
plt.scatter([coord_src_rot[i, 0] for i in range(len(coord_src_rot))], [coord_src_rot[i, 1] for i in range(len(coord_src_rot))], s=5, marker='o', zorder=10, color='steelblue', alpha=0.5)
pcaaxis = pca_dest[iz].components_.T
pca_eigenratio = pca_dest[iz].explained_variance_ratio_
plt.title('src_rot')
elif isub == 223:
plt.scatter(coord_dest[iz][:, 0], coord_dest[iz][:, 1], s=5, marker='o', zorder=10, color='red',
alpha=0.5)
pcaaxis = pca_dest[iz].components_.T
pca_eigenratio = pca_dest[iz].explained_variance_ratio_
plt.title('dest')
elif isub == 224:
plt.scatter([coord_dest_rot[i, 0] for i in range(len(coord_dest_rot))], [coord_dest_rot[i, 1] for i in range(len(coord_dest_rot))], s=5, marker='o', zorder=10, color='red', alpha=0.5)
pcaaxis = pca_src[iz].components_.T
pca_eigenratio = pca_src[iz].explained_variance_ratio_
plt.title('dest_rot')
plt.text(-2.5, -2, 'eigenvectors:', horizontalalignment='left', verticalalignment='bottom')
plt.text(-2.5, -2.8, str(pcaaxis), horizontalalignment='left', verticalalignment='bottom')
plt.text(-2.5, 2.5, 'eigenval_ratio:', horizontalalignment='left', verticalalignment='bottom')
plt.text(-2.5, 2, str(pca_eigenratio), horizontalalignment='left', verticalalignment='bottom')
plt.plot([0, pcaaxis[0, 0]], [0, pcaaxis[1, 0]], linewidth=2, color='red')
plt.plot([0, pcaaxis[0, 1]], [0, pcaaxis[1, 1]], linewidth=2, color='orange')
plt.axis([-3, 3, -3, 3])
plt.gca().set_aspect('equal', adjustable='box')
except Exception as e:
raise Exception
# sct.printv('Error on line {}'.format(sys.exc_info()[-1].tb_lineno), 1, 'warning')
# sct.printv('WARNING: '+str(e), 1, 'warning')
# plt.axis('equal')
plt.savefig(os.path.join(path_qc, 'register2d_centermassrot_pca_z' + str(iz) + '.png'))
plt.close()
# construct 3D warping matrix
warp_x[:, :, iz] = np.array([coord_forward_phy[i, 0] - coord_init_phy[i, 0] for i in range(nx * ny)]).reshape((nx, ny))
warp_y[:, :, iz] = np.array([coord_forward_phy[i, 1] - coord_init_phy[i, 1] for i in range(nx * ny)]).reshape((nx, ny))
warp_inv_x[:, :, iz] = np.array([coord_inverse_phy[i, 0] - coord_init_phy[i, 0] for i in range(nx * ny)]).reshape((nx, ny))
warp_inv_y[:, :, iz] = np.array([coord_inverse_phy[i, 1] - coord_init_phy[i, 1] for i in range(nx * ny)]).reshape((nx, ny))
logger.info('\n Done')
# Generate forward warping field (defined in destination space)
generate_warping_field(fname_dest, warp_x, warp_y, fname_warp, verbose)
generate_warping_field(fname_src, warp_inv_x, warp_inv_y, fname_warp_inv, verbose)
def main(fname_data, fname_bvecs, fname_bvals, path_out, average, verbose, remove_tmp_files):
# Initialization
start_time = time.time()
# print arguments
sct.printv('\nInput parameters:', verbose)
sct.printv(' input file ............'+fname_data, verbose)
sct.printv(' bvecs file ............'+fname_bvecs, verbose)
sct.printv(' bvals file ............'+fname_bvals, verbose)
sct.printv(' average ...............'+str(average), verbose)
# Get full path
fname_data = os.path.abspath(fname_data)
fname_bvecs = os.path.abspath(fname_bvecs)
if fname_bvals:
fname_bvals = os.path.abspath(fname_bvals)
# Extract path, file and extension
path_data, file_data, ext_data = sct.extract_fname(fname_data)
# # get output folder
# if path_out == '':
# path_out = ''
# create temporary folder
sct.printv('\nCreate temporary folder...', verbose)
path_tmp = sct.slash_at_the_end('tmp.'+time.strftime("%y%m%d%H%M%S"), 1)
sct.run('mkdir '+path_tmp, verbose)
# copy files into tmp folder and convert to nifti
sct.printv('\nCopy files into temporary folder...', verbose)
ext = '.nii'
dmri_name = 'dmri'
b0_name = 'b0'
b0_mean_name = b0_name+'_mean'
dwi_name = 'dwi'
dwi_mean_name = dwi_name+'_mean'
from sct_convert import convert
if not convert(fname_data, path_tmp+dmri_name+ext):
sct.printv('ERROR in convert.', 1, 'error')
sct.run('cp '+fname_bvecs+' '+path_tmp+'bvecs', verbose)
# go to tmp folder
os.chdir(path_tmp)
# Get size of data
im_dmri = Image(dmri_name+ext)
sct.printv('\nGet dimensions data...', verbose)
nx, ny, nz, nt, px, py, pz, pt = im_dmri.dim
sct.printv('.. '+str(nx)+' x '+str(ny)+' x '+str(nz)+' x '+str(nt), verbose)
# Identify b=0 and DWI images
print fname_bvals
index_b0, index_dwi, nb_b0, nb_dwi = identify_b0(fname_bvecs, fname_bvals, param.bval_min, verbose)
# Split into T dimension
sct.printv('\nSplit along T dimension...', verbose)
im_dmri_split_list = split_data(im_dmri, 3)
for im_d in im_dmri_split_list:
im_d.save()
# Merge b=0 images
sct.printv('\nMerge b=0...', verbose)
cmd = 'sct_image -concat t -o '+b0_name+ext+' -i '
for it in range(nb_b0):
cmd = cmd+dmri_name+'_T' + str(index_b0[it]).zfill(4)+ext+','
cmd = cmd[:-1] # remove ',' at the end of the string
# WARNING: calling concat_data in python instead of in command line causes a non understood issue
status, output = sct.run(cmd, param.verbose)
# Average b=0 images
if average:
sct.printv('\nAverage b=0...', verbose)
sct.run('sct_maths -i '+b0_name+ext+' -o '+b0_mean_name+ext+' -mean t', verbose)
# Merge DWI
cmd = 'sct_image -concat t -o '+dwi_name+ext+' -i '
for it in range(nb_dwi):
cmd = cmd+dmri_name+'_T' + str(index_dwi[it]).zfill(4) + ext+ ','
cmd = cmd[:-1] # remove ',' at the end of the string
# WARNING: calling concat_data in python instead of in command line causes a non understood issue
status, output = sct.run(cmd, param.verbose)
# Average DWI images
if average:
sct.printv('\nAverage DWI...', verbose)
sct.run('sct_maths -i '+dwi_name+ext+' -o '+dwi_mean_name+ext+' -mean t', verbose)
# if not average_data_across_dimension('dwi.nii', 'dwi_mean.nii', 3):
# sct.printv('ERROR in average_data_across_dimension', 1, 'error')
# sct.run(fsloutput + 'fslmaths dwi -Tmean dwi_mean', verbose)
# come back to parent folder
os.chdir('..')
# Generate output files
sct.printv('\nGenerate output files...', verbose)
sct.generate_output_file(path_tmp+b0_name+ext, path_out+b0_name+ext_data, verbose)
sct.generate_output_file(path_tmp+dwi_name+ext, path_out+dwi_name+ext_data, verbose)
if average:
sct.generate_output_file(path_tmp+b0_mean_name+ext, path_out+b0_mean_name+ext_data, verbose)
sct.generate_output_file(path_tmp+dwi_mean_name+ext, path_out+dwi_mean_name+ext_data, verbose)
# Remove temporary files
if remove_tmp_files == 1:
sct.printv('\nRemove temporary files...', verbose)
sct.run('rm -rf '+path_tmp, verbose)
# display elapsed time
elapsed_time = time.time() - start_time
sct.printv('\nFinished! Elapsed time: '+str(int(round(elapsed_time)))+'s', verbose)
# to view results
sct.printv('\nTo view results, type: ', verbose)
if average:
sct.printv('fslview b0 b0_mean dwi dwi_mean &\n', verbose)
else:
sct.printv('fslview b0 dwi &\n', verbose)
def apply(self):
# Initialization
fname_src = self.input_filename # source image (moving)
list_warp = self.list_warp # list of warping fields
fname_out = self.output_filename # output
fname_dest = self.fname_dest # destination image (fix)
verbose = self.verbose
remove_temp_files = self.remove_temp_files
crop_reference = self.crop # if = 1, put 0 everywhere around warping field, if = 2, real crop
interp = sct.get_interpolation('isct_antsApplyTransforms', self.interp)
# Parse list of warping fields
sct.printv('\nParse list of warping fields...', verbose)
use_inverse = []
fname_warp_list_invert = []
# list_warp = list_warp.replace(' ', '') # remove spaces
# list_warp = list_warp.split(",") # parse with comma
for idx_warp, path_warp in enumerate(self.list_warp):
# Check if this transformation should be inverted
if path_warp in self.list_warpinv:
use_inverse.append('-i')
# list_warp[idx_warp] = path_warp[1:] # remove '-'
fname_warp_list_invert += [[
use_inverse[idx_warp], list_warp[idx_warp]
]]
else:
use_inverse.append('')
fname_warp_list_invert += [[path_warp]]
path_warp = list_warp[idx_warp]
if path_warp.endswith((".nii", ".nii.gz")) \
and msct_image.Image(list_warp[idx_warp]).header.get_intent()[0] != 'vector':
raise ValueError("Displacement field in {} is invalid: should be encoded" \
" in a 5D file with vector intent code" \
" (see https://nifti.nimh.nih.gov/pub/dist/src/niftilib/nifti1.h" \
.format(path_warp))
# need to check if last warping field is an affine transfo
isLastAffine = False
path_fname, file_fname, ext_fname = sct.extract_fname(
fname_warp_list_invert[-1][-1])
if ext_fname in ['.txt', '.mat']:
isLastAffine = True
# check if destination file is 3d
if not sct.check_if_3d(fname_dest):
sct.printv('ERROR: Destination data must be 3d')
# N.B. Here we take the inverse of the warp list, because sct_WarpImageMultiTransform concatenates in the reverse order
fname_warp_list_invert.reverse()
fname_warp_list_invert = functools.reduce(lambda x, y: x + y,
fname_warp_list_invert)
# Extract path, file and extension
path_src, file_src, ext_src = sct.extract_fname(fname_src)
path_dest, file_dest, ext_dest = sct.extract_fname(fname_dest)
# Get output folder and file name
if fname_out == '':
path_out = '' # output in user's current directory
file_out = file_src + '_reg'
ext_out = ext_src
fname_out = os.path.join(path_out, file_out + ext_out)
# Get dimensions of data
sct.printv('\nGet dimensions of data...', verbose)
img_src = msct_image.Image(fname_src)
nx, ny, nz, nt, px, py, pz, pt = img_src.dim
# nx, ny, nz, nt, px, py, pz, pt = sct.get_dimension(fname_src)
sct.printv(
' ' + str(nx) + ' x ' + str(ny) + ' x ' + str(nz) + ' x ' +
str(nt), verbose)
# if 3d
if nt == 1:
# Apply transformation
sct.printv('\nApply transformation...', verbose)
if nz in [0, 1]:
dim = '2'
else:
dim = '3'
sct.run([
'isct_antsApplyTransforms', '-d', dim, '-i', fname_src, '-o',
fname_out, '-t'
] + fname_warp_list_invert + ['-r', fname_dest] + interp,
verbose=verbose,
is_sct_binary=True)
# if 4d, loop across the T dimension
else:
path_tmp = sct.tmp_create(basename="apply_transfo",
verbose=verbose)
# convert to nifti into temp folder
sct.printv(
'\nCopying input data to tmp folder and convert to nii...',
verbose)
img_src.save(os.path.join(path_tmp, "data.nii"))
sct.copy(fname_dest, os.path.join(path_tmp, file_dest + ext_dest))
fname_warp_list_tmp = []
for fname_warp in list_warp:
path_warp, file_warp, ext_warp = sct.extract_fname(fname_warp)
sct.copy(fname_warp,
os.path.join(path_tmp, file_warp + ext_warp))
fname_warp_list_tmp.append(file_warp + ext_warp)
fname_warp_list_invert_tmp = fname_warp_list_tmp[::-1]
curdir = os.getcwd()
os.chdir(path_tmp)
# split along T dimension
sct.printv('\nSplit along T dimension...', verbose)
im_dat = msct_image.Image('data.nii')
im_header = im_dat.hdr
data_split_list = sct_image.split_data(im_dat, 3)
for im in data_split_list:
im.save()
# apply transfo
sct.printv('\nApply transformation to each 3D volume...', verbose)
for it in range(nt):
file_data_split = 'data_T' + str(it).zfill(4) + '.nii'
file_data_split_reg = 'data_reg_T' + str(it).zfill(4) + '.nii'
status, output = sct.run([
'isct_antsApplyTransforms',
'-d',
'3',
'-i',
file_data_split,
'-o',
file_data_split_reg,
'-t',
] + fname_warp_list_invert_tmp + [
'-r',
file_dest + ext_dest,
] + interp,
verbose,
is_sct_binary=True)
# Merge files back
sct.printv('\nMerge file back...', verbose)
import glob
path_out, name_out, ext_out = sct.extract_fname(fname_out)
# im_list = [Image(file_name) for file_name in glob.glob('data_reg_T*.nii')]
# concat_data use to take a list of image in input, now takes a list of file names to open the files one by one (see issue #715)
fname_list = glob.glob('data_reg_T*.nii')
fname_list.sort()
im_out = sct_image.concat_data(fname_list, 3, im_header['pixdim'])
im_out.save(name_out + ext_out)
os.chdir(curdir)
sct.generate_output_file(
os.path.join(path_tmp, name_out + ext_out), fname_out)
# Delete temporary folder if specified
if int(remove_temp_files):
sct.printv('\nRemove temporary files...', verbose)
sct.rmtree(path_tmp, verbose=verbose)
# 2. crop the resulting image using dimensions from the warping field
warping_field = fname_warp_list_invert[-1]
# if last warping field is an affine transfo, we need to compute the space of the concatenate warping field:
if isLastAffine:
sct.printv(
'WARNING: the resulting image could have wrong apparent results. You should use an affine transformation as last transformation...',
verbose, 'warning')
elif crop_reference == 1:
ImageCropper(input_file=fname_out,
output_file=fname_out,
ref=warping_field,
background=0).crop()
# sct.run('sct_crop_image -i '+fname_out+' -o '+fname_out+' -ref '+warping_field+' -b 0')
elif crop_reference == 2:
ImageCropper(input_file=fname_out,
output_file=fname_out,
ref=warping_field).crop()
# sct.run('sct_crop_image -i '+fname_out+' -o '+fname_out+' -ref '+warping_field)
sct.display_viewer_syntax([fname_dest, fname_out], verbose=verbose)
def find_centerline(algo, image_fname, contrast_type, brain_bool, folder_output, remove_temp_files, centerline_fname):
"""
Assumes RPI orientation
:param algo:
:param image_fname:
:param contrast_type:
:param brain_bool:
:param folder_output:
:param remove_temp_files:
:param centerline_fname:
:return:
"""
im = Image(image_fname)
ctl_absolute_path = sct.add_suffix(im.absolutepath, "_ctr")
# isct_spine_detect requires nz > 1
if im.dim[2] == 1:
im = concat_data([im, im], dim=2)
im.hdr['dim'][3] = 2 # Needs to be change manually since dim not updated during concat_data
bool_2d = True
else:
bool_2d = False
# TODO: maybe change 'svm' for 'optic', because this is how we call it in sct_get_centerline
if algo == 'svm':
# run optic on a heatmap computed by a trained SVM+HoG algorithm
# optic_models_fname = os.path.join(path_sct, 'data', 'optic_models', '{}_model'.format(contrast_type))
# # TODO: replace with get_centerline(method=optic)
im_ctl, _, _, _ = get_centerline(im,
ParamCenterline(algo_fitting='optic', contrast=contrast_type))
elif algo == 'cnn':
# CNN parameters
dct_patch_ctr = {'t2': {'size': (80, 80), 'mean': 51.1417, 'std': 57.4408},
't2s': {'size': (80, 80), 'mean': 68.8591, 'std': 71.4659},
't1': {'size': (80, 80), 'mean': 55.7359, 'std': 64.3149},
'dwi': {'size': (80, 80), 'mean': 55.744, 'std': 45.003}}
dct_params_ctr = {'t2': {'features': 16, 'dilation_layers': 2},
't2s': {'features': 8, 'dilation_layers': 3},
't1': {'features': 24, 'dilation_layers': 3},
'dwi': {'features': 8, 'dilation_layers': 2}}
# load model
ctr_model_fname = os.path.join(sct.__sct_dir__, 'data', 'deepseg_sc_models', '{}_ctr.h5'.format(contrast_type))
ctr_model = nn_architecture_ctr(height=dct_patch_ctr[contrast_type]['size'][0],
width=dct_patch_ctr[contrast_type]['size'][1],
channels=1,
classes=1,
features=dct_params_ctr[contrast_type]['features'],
depth=2,
temperature=1.0,
padding='same',
batchnorm=True,
dropout=0.0,
dilation_layers=dct_params_ctr[contrast_type]['dilation_layers'])
ctr_model.load_weights(ctr_model_fname)
# compute the heatmap
im_heatmap, z_max = heatmap(im=im,
model=ctr_model,
patch_shape=dct_patch_ctr[contrast_type]['size'],
mean_train=dct_patch_ctr[contrast_type]['mean'],
std_train=dct_patch_ctr[contrast_type]['std'],
brain_bool=brain_bool)
im_ctl, _, _, _ = get_centerline(im_heatmap,
ParamCenterline(algo_fitting='optic', contrast=contrast_type))
if z_max is not None:
sct.printv('Cropping brain section.')
im_ctl.data[:, :, z_max:] = 0
elif algo == 'viewer':
im_labels = _call_viewer_centerline(im)
im_ctl, _, _, _ = get_centerline(im_labels, param=ParamCenterline())
elif algo == 'file':
im_ctl = Image(centerline_fname)
im_ctl.change_orientation('RPI')
else:
logger.error('The parameter "-centerline" is incorrect. Please try again.')
sys.exit(1)
# TODO: for some reason, when algo == 'file', the absolutepath is changed to None out of the method find_centerline
im_ctl.absolutepath = ctl_absolute_path
if bool_2d:
im_ctl = split_data(im_ctl, dim=2)[0]
if algo != 'viewer':
im_labels = None
# TODO: remove unecessary return params
return "dummy_file_name", im_ctl, im_labels
def moco(param):
# retrieve parameters
fsloutput = "export FSLOUTPUTTYPE=NIFTI; " # for faster processing, all outputs are in NIFTI
file_data = param.file_data
file_target = param.file_target
folder_mat = sct.slash_at_the_end(param.mat_moco, 1) # output folder of mat file
todo = param.todo
suffix = param.suffix
# file_schedule = param.file_schedule
verbose = param.verbose
ext = ".nii"
# get path of the toolbox
status, path_sct = commands.getstatusoutput("echo $SCT_DIR")
# print arguments
sct.printv("\nInput parameters:", param.verbose)
sct.printv(" Input file ............" + file_data, param.verbose)
sct.printv(" Reference file ........" + file_target, param.verbose)
sct.printv(" Polynomial degree ....." + param.param[0], param.verbose)
sct.printv(" Smoothing kernel ......" + param.param[1], param.verbose)
sct.printv(" Gradient step ........." + param.param[2], param.verbose)
sct.printv(" Metric ................" + param.param[3], param.verbose)
sct.printv(" Todo .................." + todo, param.verbose)
sct.printv(" Mask ................." + param.fname_mask, param.verbose)
sct.printv(" Output mat folder ....." + folder_mat, param.verbose)
# create folder for mat files
sct.create_folder(folder_mat)
# Get size of data
sct.printv("\nGet dimensions data...", verbose)
data_im = Image(file_data + ext)
nx, ny, nz, nt, px, py, pz, pt = data_im.dim
sct.printv((".. " + str(nx) + " x " + str(ny) + " x " + str(nz) + " x " + str(nt)), verbose)
# copy file_target to a temporary file
sct.printv("\nCopy file_target to a temporary file...", verbose)
sct.run("cp " + file_target + ext + " target.nii")
file_target = "target"
# Split data along T dimension
sct.printv("\nSplit data along T dimension...", verbose)
data_split_list = split_data(data_im, dim=3)
for im in data_split_list:
im.save()
file_data_splitT = file_data + "_T"
# Motion correction: initialization
index = np.arange(nt)
file_data_splitT_num = []
file_data_splitT_moco_num = []
failed_transfo = [0 for i in range(nt)]
file_mat = [[] for i in range(nt)]
# Motion correction: Loop across T
for indice_index in range(nt):
# create indices and display stuff
it = index[indice_index]
file_data_splitT_num.append(file_data_splitT + str(it).zfill(4))
file_data_splitT_moco_num.append(file_data + suffix + "_T" + str(it).zfill(4))
sct.printv(("\nVolume " + str((it)) + "/" + str(nt - 1) + ":"), verbose)
file_mat[it] = folder_mat + "mat.T" + str(it)
# run 3D registration
failed_transfo[it] = register(
param, file_data_splitT_num[it], file_target, file_mat[it], file_data_splitT_moco_num[it]
)
# average registered volume with target image
# N.B. use weighted averaging: (target * nb_it + moco) / (nb_it + 1)
if param.iterative_averaging and indice_index < 10 and failed_transfo[it] == 0:
sct.run("sct_maths -i " + file_target + ext + " -mul " + str(indice_index + 1) + " -o " + file_target + ext)
sct.run(
"sct_maths -i "
+ file_target
+ ext
+ " -add "
+ file_data_splitT_moco_num[it]
+ ext
+ " -o "
+ file_target
+ ext
)
sct.run("sct_maths -i " + file_target + ext + " -div " + str(indice_index + 2) + " -o " + file_target + ext)
# Replace failed transformation with the closest good one
sct.printv(("\nReplace failed transformations..."), verbose)
fT = [i for i, j in enumerate(failed_transfo) if j == 1]
gT = [i for i, j in enumerate(failed_transfo) if j == 0]
for it in range(len(fT)):
abs_dist = [abs(gT[i] - fT[it]) for i in range(len(gT))]
if not abs_dist == []:
index_good = abs_dist.index(min(abs_dist))
sct.printv(" transfo #" + str(fT[it]) + " --> use transfo #" + str(gT[index_good]), verbose)
# copy transformation
sct.run("cp " + file_mat[gT[index_good]] + "Warp.nii.gz" + " " + file_mat[fT[it]] + "Warp.nii.gz")
# apply transformation
sct.run(
"sct_apply_transfo -i "
+ file_data_splitT_num[fT[it]]
+ ".nii -d "
+ file_target
+ ".nii -w "
+ file_mat[fT[it]]
+ "Warp.nii.gz"
+ " -o "
+ file_data_splitT_moco_num[fT[it]]
+ ".nii"
+ " -x "
+ param.interp,
verbose,
)
else:
# exit program if no transformation exists.
sct.printv(
"\nERROR in " + os.path.basename(__file__) + ": No good transformation exist. Exit program.\n",
verbose,
"error",
)
sys.exit(2)
# Merge data along T
file_data_moco = file_data + suffix
if todo != "estimate":
sct.printv("\nMerge data back along T...", verbose)
# cmd = fsloutput + 'fslmerge -t ' + file_data_moco
# for indice_index in range(len(index)):
# cmd = cmd + ' ' + file_data_splitT_moco_num[indice_index]
from sct_image import concat_data
im_list = []
for indice_index in range(len(index)):
im_list.append(Image(file_data_splitT_moco_num[indice_index] + ext))
im_out = concat_data(im_list, 3)
im_out.setFileName(file_data_moco + ext)
im_out.save()
def register2d_columnwise(fname_src, fname_dest, fname_warp='warp_forward.nii.gz', fname_warp_inv='warp_inverse.nii.gz', verbose=0, path_qc='./', smoothWarpXY=1):
"""
Column-wise non-linear registration of segmentations. Based on an idea from Allan Martin.
- Assumes src/dest are segmentations (not necessarily binary), and already registered by center of mass
- Assumes src/dest are in RPI orientation.
- Split along Z, then for each slice:
- scale in R-L direction to match src/dest
- loop across R-L columns and register by (i) matching center of mass and (ii) scaling.
:param fname_src:
:param fname_dest:
:param fname_warp:
:param fname_warp_inv:
:param verbose:
:return:
"""
# initialization
th_nonzero = 0.5 # values below are considered zero
# for display stuff
if verbose == 2:
import matplotlib
matplotlib.use('Agg') # prevent display figure
import matplotlib.pyplot as plt
# Get image dimensions and retrieve nz
sct.printv('\nGet image dimensions of destination image...', verbose)
nx, ny, nz, nt, px, py, pz, pt = Image(fname_dest).dim
sct.printv(' matrix size: ' + str(nx) + ' x ' + str(ny) + ' x ' + str(nz), verbose)
sct.printv(' voxel size: ' + str(px) + 'mm x ' + str(py) + 'mm x ' + str(pz) + 'mm', verbose)
# Split source volume along z
sct.printv('\nSplit input volume...', verbose)
from sct_image import split_data
im_src = Image('src.nii')
split_source_list = split_data(im_src, 2)
for im in split_source_list:
im.save()
# Split destination volume along z
sct.printv('\nSplit destination volume...', verbose)
im_dest = Image('dest.nii')
split_dest_list = split_data(im_dest, 2)
for im in split_dest_list:
im.save()
# open image
data_src = im_src.data
data_dest = im_dest.data
if len(data_src.shape) == 2:
# reshape 2D data into pseudo 3D (only one slice)
new_shape = list(data_src.shape)
new_shape.append(1)
new_shape = tuple(new_shape)
data_src = data_src.reshape(new_shape)
data_dest = data_dest.reshape(new_shape)
# initialize forward warping field (defined in destination space)
warp_x = np.zeros(data_dest.shape)
warp_y = np.zeros(data_dest.shape)
# initialize inverse warping field (defined in source space)
warp_inv_x = np.zeros(data_src.shape)
warp_inv_y = np.zeros(data_src.shape)
# Loop across slices
sct.printv('\nEstimate columnwise transformation...', verbose)
for iz in range(0, nz):
sct.printv(str(iz) + '/' + str(nz) + '..',)
# PREPARE COORDINATES
# ============================================================
# get indices of x and y coordinates
row, col = np.indices((nx, ny))
# build 2xn array of coordinates in pixel space
# ordering of indices is as follows:
# coord_init_pix[:, 0] = 0, 0, 0, ..., 1, 1, 1..., nx, nx, nx
# coord_init_pix[:, 1] = 0, 1, 2, ..., 0, 1, 2..., 0, 1, 2
coord_init_pix = np.array([row.ravel(), col.ravel(), np.array(np.ones(len(row.ravel())) * iz)]).T
# convert coordinates to physical space
coord_init_phy = np.array(im_src.transfo_pix2phys(coord_init_pix))
# get 2d data from the selected slice
src2d = data_src[:, :, iz]
dest2d = data_dest[:, :, iz]
# julien 20161105
#<<<
# threshold at 0.5
src2d[src2d < th_nonzero] = 0
dest2d[dest2d < th_nonzero] = 0
# get non-zero coordinates, and transpose to obtain nx2 dimensions
coord_src2d = np.array(np.where(src2d > 0)).T
coord_dest2d = np.array(np.where(dest2d > 0)).T
# here we use 0.5 as threshold for non-zero value
# coord_src2d = np.array(np.where(src2d > th_nonzero)).T
# coord_dest2d = np.array(np.where(dest2d > th_nonzero)).T
#>>>
# SCALING R-L (X dimension)
# ============================================================
# sum data across Y to obtain 1D signal: src_y and dest_y
src1d = np.sum(src2d, 1)
dest1d = np.sum(dest2d, 1)
# make sure there are non-zero data in src or dest
if np.any(src1d > th_nonzero) and np.any(dest1d > th_nonzero):
# retrieve min/max of non-zeros elements (edge of the segmentation)
# julien 20161105
# <<<
src1d_min, src1d_max = min(np.where(src1d != 0)[0]), max(np.where(src1d != 0)[0])
dest1d_min, dest1d_max = min(np.where(dest1d != 0)[0]), max(np.where(dest1d != 0)[0])
# for i in range(len(src1d)):
# if src1d[i] > 0.5:
# found index above 0.5, exit loop
# break
# get indices (in continuous space) at half-maximum of upward and downward slope
# src1d_min, src1d_max = find_index_halfmax(src1d)
# dest1d_min, dest1d_max = find_index_halfmax(dest1d)
# >>>
# 1D matching between src_y and dest_y
mean_dest_x = (dest1d_max + dest1d_min) / 2
mean_src_x = (src1d_max + src1d_min) / 2
# compute x-scaling factor
Sx = (dest1d_max - dest1d_min + 1) / float(src1d_max - src1d_min + 1)
# apply transformation to coordinates
coord_src2d_scaleX = np.copy(coord_src2d) # need to use np.copy to avoid copying pointer
coord_src2d_scaleX[:, 0] = (coord_src2d[:, 0] - mean_src_x) * Sx + mean_dest_x
coord_init_pix_scaleX = np.copy(coord_init_pix)
coord_init_pix_scaleX[:, 0] = (coord_init_pix[:, 0] - mean_src_x) * Sx + mean_dest_x
coord_init_pix_scaleXinv = np.copy(coord_init_pix)
coord_init_pix_scaleXinv[:, 0] = (coord_init_pix[:, 0] - mean_dest_x) / float(Sx) + mean_src_x
# apply transformation to image
from skimage.transform import warp
row_scaleXinv = np.reshape(coord_init_pix_scaleXinv[:, 0], [nx, ny])
src2d_scaleX = warp(src2d, np.array([row_scaleXinv, col]), order=1)
# ============================================================
# COLUMN-WISE REGISTRATION (Y dimension for each Xi)
# ============================================================
coord_init_pix_scaleY = np.copy(coord_init_pix) # need to use np.copy to avoid copying pointer
coord_init_pix_scaleYinv = np.copy(coord_init_pix) # need to use np.copy to avoid copying pointer
# coord_src2d_scaleXY = np.copy(coord_src2d_scaleX) # need to use np.copy to avoid copying pointer
# loop across columns (X dimension)
for ix in range(nx):
# retrieve 1D signal along Y
src1d = src2d_scaleX[ix, :]
dest1d = dest2d[ix, :]
# make sure there are non-zero data in src or dest
if np.any(src1d > th_nonzero) and np.any(dest1d > th_nonzero):
# retrieve min/max of non-zeros elements (edge of the segmentation)
# src1d_min, src1d_max = min(np.nonzero(src1d)[0]), max(np.nonzero(src1d)[0])
# dest1d_min, dest1d_max = min(np.nonzero(dest1d)[0]), max(np.nonzero(dest1d)[0])
# 1D matching between src_y and dest_y
# Ty = (dest1d_max + dest1d_min)/2 - (src1d_max + src1d_min)/2
# Sy = (dest1d_max - dest1d_min) / float(src1d_max - src1d_min)
# apply translation and scaling to coordinates in column
# get indices (in continuous space) at half-maximum of upward and downward slope
# src1d_min, src1d_max = find_index_halfmax(src1d)
# dest1d_min, dest1d_max = find_index_halfmax(dest1d)
src1d_min, src1d_max = np.min(np.where(src1d > th_nonzero)), np.max(np.where(src1d > th_nonzero))
dest1d_min, dest1d_max = np.min(np.where(dest1d > th_nonzero)), np.max(np.where(dest1d > th_nonzero))
# 1D matching between src_y and dest_y
mean_dest_y = (dest1d_max + dest1d_min) / 2
mean_src_y = (src1d_max + src1d_min) / 2
# Tx = (dest1d_max + dest1d_min)/2 - (src1d_max + src1d_min)/2
Sy = (dest1d_max - dest1d_min + 1) / float(src1d_max - src1d_min + 1)
# apply forward transformation (in pixel space)
# below: only for debugging purpose
# coord_src2d_scaleX = np.copy(coord_src2d) # need to use np.copy to avoid copying pointer
# coord_src2d_scaleX[:, 0] = (coord_src2d[:, 0] - mean_src) * Sx + mean_dest
# coord_init_pix_scaleY = np.copy(coord_init_pix) # need to use np.copy to avoid copying pointer
# coord_init_pix_scaleY[:, 0] = (coord_init_pix[:, 0] - mean_src ) * Sx + mean_dest
range_x = list(range(ix * ny, ix * ny + nx))
coord_init_pix_scaleY[range_x, 1] = (coord_init_pix[range_x, 1] - mean_src_y) * Sy + mean_dest_y
coord_init_pix_scaleYinv[range_x, 1] = (coord_init_pix[range_x, 1] - mean_dest_y) / float(Sy) + mean_src_y
# apply transformation to image
col_scaleYinv = np.reshape(coord_init_pix_scaleYinv[:, 1], [nx, ny])
src2d_scaleXY = warp(src2d, np.array([row_scaleXinv, col_scaleYinv]), order=1)
# regularize Y warping fields
from skimage.filters import gaussian
col_scaleY = np.reshape(coord_init_pix_scaleY[:, 1], [nx, ny])
col_scaleYsmooth = gaussian(col_scaleY, smoothWarpXY)
col_scaleYinvsmooth = gaussian(col_scaleYinv, smoothWarpXY)
# apply smoothed transformation to image
src2d_scaleXYsmooth = warp(src2d, np.array([row_scaleXinv, col_scaleYinvsmooth]), order=1)
# reshape warping field as 1d
coord_init_pix_scaleY[:, 1] = col_scaleYsmooth.ravel()
coord_init_pix_scaleYinv[:, 1] = col_scaleYinvsmooth.ravel()
# display
if verbose == 2:
# FIG 1
plt.figure(figsize=(15, 3))
# plot #1
ax = plt.subplot(141)
plt.imshow(np.swapaxes(src2d, 1, 0), cmap=plt.cm.gray, interpolation='none')
plt.hold(True) # add other layer
plt.imshow(np.swapaxes(dest2d, 1, 0), cmap=plt.cm.copper, interpolation='none', alpha=0.5)
plt.title('src')
plt.xlabel('x')
plt.ylabel('y')
plt.xlim(mean_dest_x - 15, mean_dest_x + 15)
plt.ylim(mean_dest_y - 15, mean_dest_y + 15)
ax.grid(True, color='w')
# plot #2
ax = plt.subplot(142)
plt.imshow(np.swapaxes(src2d_scaleX, 1, 0), cmap=plt.cm.gray, interpolation='none')
plt.hold(True) # add other layer
plt.imshow(np.swapaxes(dest2d, 1, 0), cmap=plt.cm.copper, interpolation='none', alpha=0.5)
plt.title('src_scaleX')
plt.xlabel('x')
plt.ylabel('y')
plt.xlim(mean_dest_x - 15, mean_dest_x + 15)
plt.ylim(mean_dest_y - 15, mean_dest_y + 15)
ax.grid(True, color='w')
# plot #3
ax = plt.subplot(143)
plt.imshow(np.swapaxes(src2d_scaleXY, 1, 0), cmap=plt.cm.gray, interpolation='none')
plt.hold(True) # add other layer
plt.imshow(np.swapaxes(dest2d, 1, 0), cmap=plt.cm.copper, interpolation='none', alpha=0.5)
plt.title('src_scaleXY')
plt.xlabel('x')
plt.ylabel('y')
plt.xlim(mean_dest_x - 15, mean_dest_x + 15)
plt.ylim(mean_dest_y - 15, mean_dest_y + 15)
ax.grid(True, color='w')
# plot #4
ax = plt.subplot(144)
plt.imshow(np.swapaxes(src2d_scaleXYsmooth, 1, 0), cmap=plt.cm.gray, interpolation='none')
plt.hold(True) # add other layer
plt.imshow(np.swapaxes(dest2d, 1, 0), cmap=plt.cm.copper, interpolation='none', alpha=0.5)
plt.title('src_scaleXYsmooth (s=' + str(smoothWarpXY) + ')')
plt.xlabel('x')
plt.ylabel('y')
plt.xlim(mean_dest_x - 15, mean_dest_x + 15)
plt.ylim(mean_dest_y - 15, mean_dest_y + 15)
ax.grid(True, color='w')
# save figure
plt.savefig(os.path.join(path_qc, 'register2d_columnwise_image_z' + str(iz) + '.png'))
plt.close()
# ============================================================
# CALCULATE TRANSFORMATIONS
# ============================================================
# calculate forward transformation (in physical space)
coord_init_phy_scaleX = np.array(im_dest.transfo_pix2phys(coord_init_pix_scaleX))
coord_init_phy_scaleY = np.array(im_dest.transfo_pix2phys(coord_init_pix_scaleY))
# calculate inverse transformation (in physical space)
coord_init_phy_scaleXinv = np.array(im_src.transfo_pix2phys(coord_init_pix_scaleXinv))
coord_init_phy_scaleYinv = np.array(im_src.transfo_pix2phys(coord_init_pix_scaleYinv))
# compute displacement per pixel in destination space (for forward warping field)
warp_x[:, :, iz] = np.array([coord_init_phy_scaleXinv[i, 0] - coord_init_phy[i, 0] for i in range(nx * ny)]).reshape((nx, ny))
warp_y[:, :, iz] = np.array([coord_init_phy_scaleYinv[i, 1] - coord_init_phy[i, 1] for i in range(nx * ny)]).reshape((nx, ny))
# compute displacement per pixel in source space (for inverse warping field)
warp_inv_x[:, :, iz] = np.array([coord_init_phy_scaleX[i, 0] - coord_init_phy[i, 0] for i in range(nx * ny)]).reshape((nx, ny))
warp_inv_y[:, :, iz] = np.array([coord_init_phy_scaleY[i, 1] - coord_init_phy[i, 1] for i in range(nx * ny)]).reshape((nx, ny))
# Generate forward warping field (defined in destination space)
generate_warping_field(fname_dest, warp_x, warp_y, fname_warp, verbose)
# Generate inverse warping field (defined in source space)
generate_warping_field(fname_src, warp_inv_x, warp_inv_y, fname_warp_inv, verbose)
def eddy_correct(param):
sct.printv('\n\n\n\n===================================================',
param.verbose)
sct.printv(' Running: eddy_correct', param.verbose)
sct.printv('===================================================\n',
param.verbose)
fname_data = param.fname_data
min_norm = param.min_norm
cost_function = param.cost_function_flirt
verbose = param.verbose
sct.printv(('Input File:' + param.fname_data), verbose)
sct.printv(('Bvecs File:' + param.fname_bvecs), verbose)
# Extract path, file and extension
path_data, file_data, ext_data = sct.extract_fname(fname_data)
if param.mat_eddy == '':
param.mat_eddy = 'mat_eddy/'
if not os.path.exists(param.mat_eddy):
os.makedirs(param.mat_eddy)
mat_eddy = param.mat_eddy
# Schedule file for FLIRT
schedule_file = path_sct + '/flirtsch/schedule_TxTy_2mmScale.sch'
sct.printv(('\n.. Schedule file: ' + schedule_file), verbose)
# Swap X-Y dimension (to have X as phase-encoding direction)
if param.swapXY == 1:
sct.printv(
'\nSwap X-Y dimension (to have X as phase-encoding direction)',
verbose)
fname_data_new = 'tmp.data_swap'
cmd = fsloutput + 'fslswapdim ' + fname_data + ' -y -x -z ' + fname_data_new
status, output = sct.run(cmd, verbose)
sct.printv(('\n.. updated data file name: ' + fname_data_new), verbose)
else:
fname_data_new = fname_data
# Get size of data
sct.printv('\nGet dimensions data...', verbose)
nx, ny, nz, nt, px, py, pz, pt = Image(fname_data).dim
sct.printv(
'.. ' + str(nx) + ' x ' + str(ny) + ' x ' + str(nz) + ' x ' + str(nt),
verbose)
# split along T dimension
sct.printv('\nSplit along T dimension...', verbose)
from sct_image import split_data
im_to_split = Image(fname_data_new + '.nii')
im_split_list = split_data(im_to_split, 3)
for im in im_split_list:
im.save()
# cmd = fsloutput + 'fslsplit ' + fname_data_new + ' ' + file_data + '_T'
# status, output = sct.run(cmd,verbose)
# Slice-wise or Volume based method
if param.slicewise:
nb_loops = nz
file_suffix = []
for iZ in range(nz):
file_suffix.append('_Z' + str(iZ).zfill(4))
else:
nb_loops = 1
file_suffix = ['']
# Identify pairs of opposite gradient directions
sct.printv('\nIdentify pairs of opposite gradient directions...', verbose)
# Open bvecs file
sct.printv('\nOpen bvecs file...', verbose)
bvecs = []
with open(param.fname_bvecs) as f:
for line in f:
bvecs_new = map(float, line.split())
bvecs.append(bvecs_new)
# Check if bvecs file is nx3
if not len(bvecs[0][:]) == 3:
sct.printv(
'.. WARNING: bvecs file is 3xn instead of nx3. Consider using sct_dmri_transpose_bvecs.',
verbose)
sct.printv('Transpose bvecs...', verbose)
# transpose bvecs
bvecs = zip(*bvecs)
bvecs = np.array(bvecs)
opposite_gradients_iT = []
opposite_gradients_jT = []
index_identified = []
index_b0 = []
for iT in range(nt - 1):
if np.linalg.norm(bvecs[iT, :]) != 0:
if iT not in index_identified:
jT = iT + 1
if np.linalg.norm((bvecs[iT, :] + bvecs[jT, :])) < min_norm:
sct.printv(('.. Opposite gradient for #' + str(iT) +
' is: #' + str(jT)), verbose)
opposite_gradients_iT.append(iT)
opposite_gradients_jT.append(jT)
index_identified.append(iT)
else:
index_b0.append(iT)
sct.printv(
('.. Opposite gradient for #' + str(iT) + ' is: NONE (b=0)'),
verbose)
nb_oppositeGradients = len(opposite_gradients_iT)
sct.printv(
('.. Number of gradient directions: ' + str(2 * nb_oppositeGradients) +
' (2*' + str(nb_oppositeGradients) + ')'), verbose)
sct.printv('.. Index b=0: ' + str(index_b0), verbose)
# =========================================================================
# Find transformation
# =========================================================================
for iN in range(nb_oppositeGradients):
i_plus = opposite_gradients_iT[iN]
i_minus = opposite_gradients_jT[iN]
sct.printv(('\nFinding affine transformation between volumes #' +
str(i_plus) + ' and #' + str(i_minus) + ' (' + str(iN) +
'/' + str(nb_oppositeGradients) + ')'), verbose)
sct.printv(
'------------------------------------------------------------------------------------\n',
verbose)
# Slicewise correction
if param.slicewise:
sct.printv('\nSplit volumes across Z...', verbose)
fname_plus = file_data + '_T' + str(i_plus).zfill(4)
fname_plus_Z = file_data + '_T' + str(i_plus).zfill(4) + '_Z'
im_plus = Image(fname_plus + '.nii')
im_plus_split_list = split_data(im_plus, 2)
for im_p in im_plus_split_list:
im_p.save()
# cmd = fsloutput + 'fslsplit ' + fname_plus + ' ' + fname_plus_Z + ' -z'
# status, output = sct.run(cmd,verbose)
fname_minus = file_data + '_T' + str(i_minus).zfill(4)
fname_minus_Z = file_data + '_T' + str(i_minus).zfill(4) + '_Z'
im_minus = Image(fname_minus + '.nii')
im_minus_split_list = split_data(im_minus, 2)
for im_m in im_minus_split_list:
im_m.save(
) # cmd = fsloutput + 'fslsplit ' + fname_minus + ' ' + fname_minus_Z + ' -z'
# status, output = sct.run(cmd,verbose)
# loop across Z
for iZ in range(nb_loops):
fname_plus = file_data + '_T' + str(i_plus).zfill(
4) + file_suffix[iZ]
fname_minus = file_data + '_T' + str(i_minus).zfill(
4) + file_suffix[iZ]
# Find transformation on opposite gradient directions
sct.printv(
'\nFind transformation for each pair of opposite gradient directions...',
verbose)
fname_plus_corr = file_data + '_T' + str(i_plus).zfill(
4) + file_suffix[iZ] + '_corr_'
omat = 'mat_' + file_data + '_T' + str(i_plus).zfill(
4) + file_suffix[iZ] + '.txt'
cmd = fsloutput + 'flirt -in ' + fname_plus + ' -ref ' + fname_minus + ' -paddingsize 3 -schedule ' + schedule_file + ' -verbose 2 -omat ' + omat + ' -cost ' + cost_function + ' -forcescaling'
status, output = sct.run(cmd, verbose)
file = open(omat)
Matrix = np.loadtxt(file)
file.close()
M = Matrix[0:4, 0:4]
sct.printv(('.. Transformation matrix:\n' + str(M)), verbose)
sct.printv(('.. Output matrix file: ' + omat), verbose)
# Divide affine transformation by two
sct.printv('\nDivide affine transformation by two...', verbose)
A = (M - np.identity(4)) / 2
Mplus = np.identity(4) + A
omat_plus = mat_eddy + 'mat.T' + str(i_plus) + '_Z' + str(
iZ) + '.txt'
file = open(omat_plus, 'w')
np.savetxt(omat_plus,
Mplus,
fmt='%.6e',
delimiter=' ',
newline='\n',
header='',
footer='',
comments='#')
file.close()
sct.printv(('.. Output matrix file (plus): ' + omat_plus), verbose)
Mminus = np.identity(4) - A
omat_minus = mat_eddy + 'mat.T' + str(i_minus) + '_Z' + str(
iZ) + '.txt'
file = open(omat_minus, 'w')
np.savetxt(omat_minus,
Mminus,
fmt='%.6e',
delimiter=' ',
newline='\n',
header='',
footer='',
comments='#')
file.close()
sct.printv(('.. Output matrix file (minus): ' + omat_minus),
verbose)
# =========================================================================
# Apply affine transformation
# =========================================================================
sct.printv('\nApply affine transformation matrix', verbose)
sct.printv(
'------------------------------------------------------------------------------------\n',
verbose)
for iN in range(nb_oppositeGradients):
for iFile in range(2):
if iFile == 0:
i_file = opposite_gradients_iT[iN]
else:
i_file = opposite_gradients_jT[iN]
for iZ in range(nb_loops):
fname = file_data + '_T' + str(i_file).zfill(
4) + file_suffix[iZ]
fname_corr = fname + '_corr_' + '__div2'
omat = mat_eddy + 'mat.T' + str(i_file) + '_Z' + str(
iZ) + '.txt'
cmd = fsloutput + 'flirt -in ' + fname + ' -ref ' + fname + ' -out ' + fname_corr + ' -init ' + omat + ' -applyxfm -paddingsize 3 -interp ' + param.interp
status, output = sct.run(cmd, verbose)
# =========================================================================
# Merge back across Z
# =========================================================================
sct.printv('\nMerge across Z', verbose)
sct.printv(
'------------------------------------------------------------------------------------\n',
verbose)
for iN in range(nb_oppositeGradients):
i_plus = opposite_gradients_iT[iN]
fname_plus_corr = file_data + '_T' + str(i_plus).zfill(
4) + '_corr_' + '__div2'
cmd = fsloutput + 'fslmerge -z ' + fname_plus_corr
for iZ in range(nz):
fname_plus_Z_corr = file_data + '_T' + str(i_plus).zfill(
4) + file_suffix[iZ] + '_corr_' + '__div2'
cmd = cmd + ' ' + fname_plus_Z_corr
status, output = sct.run(cmd, verbose)
i_minus = opposite_gradients_jT[iN]
fname_minus_corr = file_data + '_T' + str(i_minus).zfill(
4) + '_corr_' + '__div2'
cmd = fsloutput + 'fslmerge -z ' + fname_minus_corr
for iZ in range(nz):
fname_minus_Z_corr = file_data + '_T' + str(i_minus).zfill(
4) + file_suffix[iZ] + '_corr_' + '__div2'
cmd = cmd + ' ' + fname_minus_Z_corr
status, output = sct.run(cmd, verbose)
# =========================================================================
# Merge files back
# =========================================================================
sct.printv('\nMerge back across T...', verbose)
sct.printv(
'------------------------------------------------------------------------------------\n',
verbose)
fname_data_corr = param.output_path + file_data + '_eddy'
cmd = fsloutput + 'fslmerge -t ' + fname_data_corr
path_tmp = os.getcwd()
for iT in range(nt):
if os.path.isfile((path_tmp + '/' + file_data + '_T' +
str(iT).zfill(4) + '_corr_' + '__div2.nii')):
fname_data_corr_3d = file_data + '_T' + str(iT).zfill(
4) + '_corr_' + '__div2'
elif iT in index_b0:
fname_data_corr_3d = file_data + '_T' + str(iT).zfill(4)
cmd = cmd + ' ' + fname_data_corr_3d
status, output = sct.run(cmd, verbose)
# Swap back X-Y dimensions
if param.swapXY == 1:
fname_data_final = fname_data
sct.printv('\nSwap back X-Y dimensions', verbose)
cmd = fsloutput + 'fslswapdim ' + fname_data_corr + ' -y -x -z ' + fname_data_final
status, output = sct.run(cmd, verbose)
else:
fname_data_final = fname_data_corr
sct.printv(('... File created: ' + fname_data_final), verbose)
sct.printv('\n===================================================',
verbose)
sct.printv(' Completed: eddy_correct', verbose)
sct.printv('===================================================\n\n\n',
verbose)
def register2d(fname_src, fname_dest, fname_mask='', fname_warp='warp_forward.nii.gz', fname_warp_inv='warp_inverse.nii.gz', paramreg=Paramreg(step='0', type='im', algo='Translation', metric='MI', iter='5', shrink='1', smooth='0', gradStep='0.5'),
ants_registration_params={'rigid': '', 'affine': '', 'compositeaffine': '', 'similarity': '', 'translation': '', 'bspline': ',10', 'gaussiandisplacementfield': ',3,0',
'bsplinedisplacementfield': ',5,10', 'syn': ',3,0', 'bsplinesyn': ',1,3'}, verbose=0):
"""Slice-by-slice registration of two images.
We first split the 3D images into 2D images (and the mask if inputted). Then we register slices of the two images
that physically correspond to one another looking at the physical origin of each image. The images can be of
different sizes but the destination image must be smaller thant the input image. We do that using antsRegistration
in 2D. Once this has been done for each slices, we gather the results and return them.
Algorithms implemented: translation, rigid, affine, syn and BsplineSyn.
N.B.: If the mask is inputted, it must also be 3D and it must be in the same space as the destination image.
input:
fname_source: name of moving image (type: string)
fname_dest: name of fixed image (type: string)
mask[optional]: name of mask file (type: string) (parameter -x of antsRegistration)
fname_warp: name of output 3d forward warping field
fname_warp_inv: name of output 3d inverse warping field
paramreg[optional]: parameters of antsRegistration (type: Paramreg class from sct_register_multimodal)
ants_registration_params[optional]: specific algorithm's parameters for antsRegistration (type: dictionary)
output:
if algo==translation:
x_displacement: list of translation along x axis for each slice (type: list)
y_displacement: list of translation along y axis for each slice (type: list)
if algo==rigid:
x_displacement: list of translation along x axis for each slice (type: list)
y_displacement: list of translation along y axis for each slice (type: list)
theta_rotation: list of rotation angle in radian (and in ITK's coordinate system) for each slice (type: list)
if algo==affine or algo==syn or algo==bsplinesyn:
creation of two 3D warping fields (forward and inverse) that are the concatenations of the slice-by-slice
warps.
"""
# set metricSize
if paramreg.metric == 'MI':
metricSize = '32' # corresponds to number of bins
else:
metricSize = '4' # corresponds to radius (for CC, MeanSquares...)
# Get image dimensions and retrieve nz
sct.printv('\nGet image dimensions of destination image...', verbose)
nx, ny, nz, nt, px, py, pz, pt = Image(fname_dest).dim
sct.printv('.. matrix size: ' + str(nx) + ' x ' + str(ny) + ' x ' + str(nz), verbose)
sct.printv('.. voxel size: ' + str(px) + 'mm x ' + str(py) + 'mm x ' + str(pz) + 'mm', verbose)
# Split input volume along z
sct.printv('\nSplit input volume...', verbose)
from sct_image import split_data
im_src = Image('src.nii')
split_source_list = split_data(im_src, 2)
for im in split_source_list:
im.save()
# Split destination volume along z
sct.printv('\nSplit destination volume...', verbose)
im_dest = Image('dest.nii')
split_dest_list = split_data(im_dest, 2)
for im in split_dest_list:
im.save()
# Split mask volume along z
if fname_mask != '':
sct.printv('\nSplit mask volume...', verbose)
im_mask = Image('mask.nii.gz')
split_mask_list = split_data(im_mask, 2)
for im in split_mask_list:
im.save()
# coord_origin_dest = im_dest.transfo_pix2phys([[0,0,0]])
# coord_origin_input = im_src.transfo_pix2phys([[0,0,0]])
# coord_diff_origin = (np.asarray(coord_origin_dest[0]) - np.asarray(coord_origin_input[0])).tolist()
# [x_o, y_o, z_o] = [coord_diff_origin[0] * 1.0/px, coord_diff_origin[1] * 1.0/py, coord_diff_origin[2] * 1.0/pz]
# initialization
if paramreg.algo in ['Translation']:
x_displacement = [0 for i in range(nz)]
y_displacement = [0 for i in range(nz)]
theta_rotation = [0 for i in range(nz)]
if paramreg.algo in ['Rigid', 'Affine', 'BSplineSyN', 'SyN']:
list_warp = []
list_warp_inv = []
# loop across slices
for i in range(nz):
# set masking
sct.printv('Registering slice ' + str(i) + '/' + str(nz - 1) + '...', verbose)
num = numerotation(i)
prefix_warp2d = 'warp2d_' + num
# if mask is used, prepare command for ANTs
if fname_mask != '':
masking = ['-x', 'mask_Z' + num + '.nii.gz']
else:
masking = []
# main command for registration
# TODO fixup isct_ants* parsers
cmd = ['isct_antsRegistration',
'--dimensionality', '2',
'--transform', paramreg.algo + '[' + str(paramreg.gradStep) + ants_registration_params[paramreg.algo.lower()] + ']',
'--metric', paramreg.metric + '[dest_Z' + num + '.nii' + ',src_Z' + num + '.nii' + ',1,' + metricSize + ']', #[fixedImage,movingImage,metricWeight +nb_of_bins (MI) or radius (other)
'--convergence', str(paramreg.iter),
'--shrink-factors', str(paramreg.shrink),
'--smoothing-sigmas', str(paramreg.smooth) + 'mm',
'--output', '[' + prefix_warp2d + ',src_Z' + num + '_reg.nii]', #--> file.mat (contains Tx,Ty, theta)
'--interpolation', 'BSpline[3]',
'--verbose', '1',
] + masking
# add init translation
if not paramreg.init == '':
init_dict = {'geometric': '0', 'centermass': '1', 'origin': '2'}
cmd += ['-r', '[dest_Z' + num + '.nii' + ',src_Z' + num + '.nii,' + init_dict[paramreg.init] + ']']
try:
# run registration
sct.run(cmd)
if paramreg.algo in ['Translation']:
file_mat = prefix_warp2d + '0GenericAffine.mat'
matfile = loadmat(file_mat, struct_as_record=True)
array_transfo = matfile['AffineTransform_double_2_2']
x_displacement[i] = array_transfo[4][0] # Tx in ITK'S coordinate system
y_displacement[i] = array_transfo[5][0] # Ty in ITK'S and fslview's coordinate systems
theta_rotation[i] = asin(array_transfo[2]) # angle of rotation theta in ITK'S coordinate system (minus theta for fslview)
if paramreg.algo in ['Rigid', 'Affine', 'BSplineSyN', 'SyN']:
# List names of 2d warping fields for subsequent merge along Z
file_warp2d = prefix_warp2d + '0Warp.nii.gz'
file_warp2d_inv = prefix_warp2d + '0InverseWarp.nii.gz'
list_warp.append(file_warp2d)
list_warp_inv.append(file_warp2d_inv)
if paramreg.algo in ['Rigid', 'Affine']:
# Generating null 2d warping field (for subsequent concatenation with affine transformation)
# TODO fixup isct_ants* parsers
sct.run(['isct_antsRegistration',
'-d', '2',
'-t', 'SyN[1,1,1]',
'-c', '0',
'-m', 'MI[dest_Z' + num + '.nii,src_Z' + num + '.nii,1,32]',
'-o', 'warp2d_null',
'-f', '1',
'-s', '0',
])
# --> outputs: warp2d_null0Warp.nii.gz, warp2d_null0InverseWarp.nii.gz
file_mat = prefix_warp2d + '0GenericAffine.mat'
# Concatenating mat transfo and null 2d warping field to obtain 2d warping field of affine transformation
sct.run(['isct_ComposeMultiTransform', '2', file_warp2d, '-R', 'dest_Z' + num + '.nii', 'warp2d_null0Warp.nii.gz', file_mat])
sct.run(['isct_ComposeMultiTransform', '2', file_warp2d_inv, '-R', 'src_Z' + num + '.nii', 'warp2d_null0InverseWarp.nii.gz', '-i', file_mat])
# if an exception occurs with ants, take the last value for the transformation
# TODO: DO WE NEED TO DO THAT??? (julien 2016-03-01)
except Exception as e:
sct.printv('ERROR: Exception occurred.\n' + str(e), 1, 'error')
# Merge warping field along z
sct.printv('\nMerge warping fields along z...', verbose)
if paramreg.algo in ['Translation']:
# convert to array
x_disp_a = np.asarray(x_displacement)
y_disp_a = np.asarray(y_displacement)
theta_rot_a = np.asarray(theta_rotation)
# Generate warping field
generate_warping_field('dest.nii', x_disp_a, y_disp_a, fname_warp=fname_warp) #name_warp= 'step'+str(paramreg.step)
# Inverse warping field
generate_warping_field('src.nii', -x_disp_a, -y_disp_a, fname_warp=fname_warp_inv)
if paramreg.algo in ['Rigid', 'Affine', 'BSplineSyN', 'SyN']:
from sct_image import concat_warp2d
# concatenate 2d warping fields along z
concat_warp2d(list_warp, fname_warp, 'dest.nii')
concat_warp2d(list_warp_inv, fname_warp_inv, 'src.nii')
def register2d_centermassrot(fname_src, fname_dest, fname_warp='warp_forward.nii.gz', fname_warp_inv='warp_inverse.nii.gz', rot=1, poly=0, path_qc='./', verbose=0, pca_eigenratio_th=1.6):
"""
Rotate the source image to match the orientation of the destination image, using the first and second eigenvector
of the PCA. This function should be used on segmentations (not images).
This works for 2D and 3D images. If 3D, it splits the image and performs the rotation slice-by-slice.
input:
fname_source: name of moving image (type: string)
fname_dest: name of fixed image (type: string)
fname_warp: name of output 3d forward warping field
fname_warp_inv: name of output 3d inverse warping field
rot: estimate rotation with PCA (type: int)
poly: degree of polynomial regularization along z for rotation angle (type: int). 0: no regularization
verbose:
output:
none
"""
if verbose == 2:
import matplotlib
matplotlib.use('Agg') # prevent display figure
import matplotlib.pyplot as plt
# Get image dimensions and retrieve nz
sct.printv('\nGet image dimensions of destination image...', verbose)
nx, ny, nz, nt, px, py, pz, pt = Image(fname_dest).dim
sct.printv(' matrix size: '+str(nx)+' x '+str(ny)+' x '+str(nz), verbose)
sct.printv(' voxel size: '+str(px)+'mm x '+str(py)+'mm x '+str(pz)+'mm', verbose)
# Split source volume along z
sct.printv('\nSplit input volume...', verbose)
from sct_image import split_data
im_src = Image('src.nii')
split_source_list = split_data(im_src, 2)
for im in split_source_list:
im.save()
# Split destination volume along z
sct.printv('\nSplit destination volume...', verbose)
im_dest = Image('dest.nii')
split_dest_list = split_data(im_dest, 2)
for im in split_dest_list:
im.save()
# display image
data_src = im_src.data
data_dest = im_dest.data
if len(data_src.shape) == 2:
# reshape 2D data into pseudo 3D (only one slice)
new_shape = list(data_src.shape)
new_shape.append(1)
new_shape = tuple(new_shape)
data_src = data_src.reshape(new_shape)
data_dest = data_dest.reshape(new_shape)
# initialize displacement and rotation
coord_src = [None] * nz
pca_src = [None] * nz
coord_dest = [None] * nz
pca_dest = [None] * nz
centermass_src = np.zeros([nz, 2])
centermass_dest = np.zeros([nz, 2])
# displacement_forward = np.zeros([nz, 2])
# displacement_inverse = np.zeros([nz, 2])
angle_src_dest = np.zeros(nz)
z_nonzero = []
# Loop across slices
for iz in range(0, nz):
try:
# compute PCA and get center or mass
coord_src[iz], pca_src[iz], centermass_src[iz, :] = compute_pca(data_src[:, :, iz])
coord_dest[iz], pca_dest[iz], centermass_dest[iz, :] = compute_pca(data_dest[:, :, iz])
# compute (src,dest) angle for first eigenvector
if rot == 1:
eigenv_src = pca_src[iz].components_.T[0][0], pca_src[iz].components_.T[1][0] # pca_src.components_.T[0]
eigenv_dest = pca_dest[iz].components_.T[0][0], pca_dest[iz].components_.T[1][0] # pca_dest.components_.T[0]
angle_src_dest[iz] = angle_between(eigenv_src, eigenv_dest)
# check if ratio between the two eigenvectors is high enough to prevent poor robustness
if pca_src[iz].explained_variance_ratio_[0] / pca_src[iz].explained_variance_ratio_[1] < pca_eigenratio_th:
angle_src_dest[iz] = 0
if pca_dest[iz].explained_variance_ratio_[0] / pca_dest[iz].explained_variance_ratio_[1] < pca_eigenratio_th:
angle_src_dest[iz] = 0
# append to list of z_nonzero
z_nonzero.append(iz)
# if one of the slice is empty, ignore it
except ValueError:
sct.printv('WARNING: Slice #' + str(iz) + ' is empty. It will be ignored.', verbose, 'warning')
# regularize rotation
if not poly == 0 and rot == 1:
from msct_smooth import polynomial_fit
angle_src_dest_regularized = polynomial_fit(z_nonzero, angle_src_dest[z_nonzero], poly)[0]
# display
if verbose == 2:
plt.plot(180 * angle_src_dest[z_nonzero] / np.pi)
plt.plot(180 * angle_src_dest_regularized / np.pi, 'r', linewidth=2)
plt.grid()
plt.xlabel('z')
plt.ylabel('Angle (deg)')
plt.savefig(path_qc+'register2d_centermassrot_regularize_rotation.png')
plt.close()
# update variable
angle_src_dest[z_nonzero] = angle_src_dest_regularized
# initialize warping fields
# N.B. forward transfo is defined in destination space and inverse transfo is defined in the source space
warp_x = np.zeros(data_dest.shape)
warp_y = np.zeros(data_dest.shape)
warp_inv_x = np.zeros(data_src.shape)
warp_inv_y = np.zeros(data_src.shape)
# construct 3D warping matrix
for iz in z_nonzero:
print str(iz)+'/'+str(nz)+'..',
# get indices of x and y coordinates
row, col = np.indices((nx, ny))
# build 2xn array of coordinates in pixel space
coord_init_pix = np.array([row.ravel(), col.ravel(), np.array(np.ones(len(row.ravel())) * iz)]).T
# convert coordinates to physical space
coord_init_phy = np.array(im_src.transfo_pix2phys(coord_init_pix))
# get centermass coordinates in physical space
centermass_src_phy = im_src.transfo_pix2phys([[centermass_src[iz, :].T[0], centermass_src[iz, :].T[1], iz]])[0]
centermass_dest_phy = im_src.transfo_pix2phys([[centermass_dest[iz, :].T[0], centermass_dest[iz, :].T[1], iz]])[0]
# build rotation matrix
R = np.matrix(((cos(angle_src_dest[iz]), sin(angle_src_dest[iz])), (-sin(angle_src_dest[iz]), cos(angle_src_dest[iz]))))
# build 3D rotation matrix
R3d = np.eye(3)
R3d[0:2, 0:2] = R
# apply forward transformation (in physical space)
coord_forward_phy = np.array(np.dot((coord_init_phy - np.transpose(centermass_dest_phy)), R3d) + np.transpose(centermass_src_phy))
# apply inverse transformation (in physical space)
coord_inverse_phy = np.array(np.dot((coord_init_phy - np.transpose(centermass_src_phy)), R3d.T) + np.transpose(centermass_dest_phy))
# display rotations
if verbose == 2 and not angle_src_dest[iz] == 0:
# compute new coordinates
coord_src_rot = coord_src[iz] * R
coord_dest_rot = coord_dest[iz] * R.T
# generate figure
plt.figure('iz=' + str(iz) + ', angle_src_dest=' + str(angle_src_dest[iz]), figsize=(9, 9))
# plt.ion() # enables interactive mode (allows keyboard interruption)
# plt.title('iz='+str(iz))
for isub in [221, 222, 223, 224]:
# plt.figure
plt.subplot(isub)
# ax = matplotlib.pyplot.axis()
if isub == 221:
plt.scatter(coord_src[iz][:, 0], coord_src[iz][:, 1], s=5, marker='o', zorder=10, color='steelblue',
alpha=0.5)
pcaaxis = pca_src[iz].components_.T
pca_eigenratio = pca_src[iz].explained_variance_ratio_
plt.title('src')
elif isub == 222:
plt.scatter(coord_src_rot[:, 0], coord_src_rot[:, 1], s=5, marker='o', zorder=10,
color='steelblue',
alpha=0.5)
pcaaxis = pca_dest[iz].components_.T
pca_eigenratio = pca_dest[iz].explained_variance_ratio_
plt.title('src_rot')
elif isub == 223:
plt.scatter(coord_dest[iz][:, 0], coord_dest[iz][:, 1], s=5, marker='o', zorder=10, color='red',
alpha=0.5)
pcaaxis = pca_dest[iz].components_.T
pca_eigenratio = pca_dest[iz].explained_variance_ratio_
plt.title('dest')
elif isub == 224:
plt.scatter(coord_dest_rot[:, 0], coord_dest_rot[:, 1], s=5, marker='o', zorder=10, color='red',
alpha=0.5)
pcaaxis = pca_src[iz].components_.T
pca_eigenratio = pca_src[iz].explained_variance_ratio_
plt.title('dest_rot')
plt.text(-2.5, -2, 'eigenvectors:', horizontalalignment='left', verticalalignment='bottom')
plt.text(-2.5, -2.8, str(pcaaxis), horizontalalignment='left', verticalalignment='bottom')
plt.text(-2.5, 2.5, 'eigenval_ratio:', horizontalalignment='left', verticalalignment='bottom')
plt.text(-2.5, 2, str(pca_eigenratio), horizontalalignment='left', verticalalignment='bottom')
plt.plot([0, pcaaxis[0, 0]], [0, pcaaxis[1, 0]], linewidth=2, color='red')
plt.plot([0, pcaaxis[0, 1]], [0, pcaaxis[1, 1]], linewidth=2, color='orange')
plt.axis([-3, 3, -3, 3])
plt.gca().set_aspect('equal', adjustable='box')
# plt.axis('equal')
plt.savefig(path_qc + 'register2d_centermassrot_pca_z' + str(iz) + '.png')
plt.close()
# construct 3D warping matrix
warp_x[:, :, iz] = np.array([coord_forward_phy[i, 0] - coord_init_phy[i, 0] for i in xrange(nx * ny)]).reshape((nx, ny))
warp_y[:, :, iz] = np.array([coord_forward_phy[i, 1] - coord_init_phy[i, 1] for i in xrange(nx * ny)]).reshape((nx, ny))
warp_inv_x[:, :, iz] = np.array([coord_inverse_phy[i, 0] - coord_init_phy[i, 0] for i in xrange(nx * ny)]).reshape((nx, ny))
warp_inv_y[:, :, iz] = np.array([coord_inverse_phy[i, 1] - coord_init_phy[i, 1] for i in xrange(nx * ny)]).reshape((nx, ny))
# Generate forward warping field (defined in destination space)
generate_warping_field(fname_dest, warp_x, warp_y, fname_warp, verbose)
generate_warping_field(fname_src, warp_inv_x, warp_inv_y, fname_warp_inv, verbose)
def register2d_centermassrot(fname_src, fname_dest, fname_warp='warp_forward.nii.gz', fname_warp_inv='warp_inverse.nii.gz', rot=1, poly=0, path_qc='./', verbose=0, pca_eigenratio_th=1.6):
"""
Rotate the source image to match the orientation of the destination image, using the first and second eigenvector
of the PCA. This function should be used on segmentations (not images).
This works for 2D and 3D images. If 3D, it splits the image and performs the rotation slice-by-slice.
input:
fname_source: name of moving image (type: string)
fname_dest: name of fixed image (type: string)
fname_warp: name of output 3d forward warping field
fname_warp_inv: name of output 3d inverse warping field
rot: estimate rotation with PCA (type: int)
poly: degree of polynomial regularization along z for rotation angle (type: int). 0: no regularization
verbose:
output:
none
"""
if verbose == 2:
import matplotlib
matplotlib.use('Agg') # prevent display figure
import matplotlib.pyplot as plt
# Get image dimensions and retrieve nz
sct.printv('\nGet image dimensions of destination image...', verbose)
nx, ny, nz, nt, px, py, pz, pt = Image(fname_dest).dim
sct.printv(' matrix size: ' + str(nx) + ' x ' + str(ny) + ' x ' + str(nz), verbose)
sct.printv(' voxel size: ' + str(px) + 'mm x ' + str(py) + 'mm x ' + str(pz) + 'mm', verbose)
# Split source volume along z
sct.printv('\nSplit input volume...', verbose)
from sct_image import split_data
im_src = Image('src.nii')
split_source_list = split_data(im_src, 2)
for im in split_source_list:
im.save()
# Split destination volume along z
sct.printv('\nSplit destination volume...', verbose)
im_dest = Image('dest.nii')
split_dest_list = split_data(im_dest, 2)
for im in split_dest_list:
im.save()
# display image
data_src = im_src.data
data_dest = im_dest.data
if len(data_src.shape) == 2:
# reshape 2D data into pseudo 3D (only one slice)
new_shape = list(data_src.shape)
new_shape.append(1)
new_shape = tuple(new_shape)
data_src = data_src.reshape(new_shape)
data_dest = data_dest.reshape(new_shape)
# initialize displacement and rotation
coord_src = [None] * nz
pca_src = [None] * nz
coord_dest = [None] * nz
pca_dest = [None] * nz
centermass_src = np.zeros([nz, 2])
centermass_dest = np.zeros([nz, 2])
# displacement_forward = np.zeros([nz, 2])
# displacement_inverse = np.zeros([nz, 2])
angle_src_dest = np.zeros(nz)
z_nonzero = []
# Loop across slices
for iz in range(0, nz):
try:
# compute PCA and get center or mass
coord_src[iz], pca_src[iz], centermass_src[iz, :] = compute_pca(data_src[:, :, iz])
coord_dest[iz], pca_dest[iz], centermass_dest[iz, :] = compute_pca(data_dest[:, :, iz])
# compute (src,dest) angle for first eigenvector
if rot == 1:
eigenv_src = pca_src[iz].components_.T[0][0], pca_src[iz].components_.T[1][0] # pca_src.components_.T[0]
eigenv_dest = pca_dest[iz].components_.T[0][0], pca_dest[iz].components_.T[1][0] # pca_dest.components_.T[0]
angle_src_dest[iz] = angle_between(eigenv_src, eigenv_dest)
# check if ratio between the two eigenvectors is high enough to prevent poor robustness
if pca_src[iz].explained_variance_ratio_[0] / pca_src[iz].explained_variance_ratio_[1] < pca_eigenratio_th:
angle_src_dest[iz] = 0
if pca_dest[iz].explained_variance_ratio_[0] / pca_dest[iz].explained_variance_ratio_[1] < pca_eigenratio_th:
angle_src_dest[iz] = 0
# append to list of z_nonzero
z_nonzero.append(iz)
# if one of the slice is empty, ignore it
except ValueError:
sct.printv('WARNING: Slice #' + str(iz) + ' is empty. It will be ignored.', verbose, 'warning')
# regularize rotation
if not poly == 0 and rot == 1:
from msct_smooth import polynomial_fit
angle_src_dest_regularized = polynomial_fit(z_nonzero, angle_src_dest[z_nonzero], poly)[0]
# display
if verbose == 2:
plt.plot(180 * angle_src_dest[z_nonzero] / np.pi)
plt.plot(180 * angle_src_dest_regularized / np.pi, 'r', linewidth=2)
plt.grid()
plt.xlabel('z')
plt.ylabel('Angle (deg)')
plt.savefig(os.path.join(path_qc, 'register2d_centermassrot_regularize_rotation.png'))
plt.close()
# update variable
angle_src_dest[z_nonzero] = angle_src_dest_regularized
# initialize warping fields
# N.B. forward transfo is defined in destination space and inverse transfo is defined in the source space
warp_x = np.zeros(data_dest.shape)
warp_y = np.zeros(data_dest.shape)
warp_inv_x = np.zeros(data_src.shape)
warp_inv_y = np.zeros(data_src.shape)
# construct 3D warping matrix
for iz in z_nonzero:
sct.no_new_line_log('{}/{}..'.format(iz + 1, nz))
# get indices of x and y coordinates
row, col = np.indices((nx, ny))
# build 2xn array of coordinates in pixel space
coord_init_pix = np.array([row.ravel(), col.ravel(), np.array(np.ones(len(row.ravel())) * iz)]).T
# convert coordinates to physical space
coord_init_phy = np.array(im_src.transfo_pix2phys(coord_init_pix))
# get centermass coordinates in physical space
centermass_src_phy = im_src.transfo_pix2phys([[centermass_src[iz, :].T[0], centermass_src[iz, :].T[1], iz]])[0]
centermass_dest_phy = im_src.transfo_pix2phys([[centermass_dest[iz, :].T[0], centermass_dest[iz, :].T[1], iz]])[0]
# build rotation matrix
R = np.matrix(((cos(angle_src_dest[iz]), sin(angle_src_dest[iz])), (-sin(angle_src_dest[iz]), cos(angle_src_dest[iz]))))
# build 3D rotation matrix
R3d = np.eye(3)
R3d[0:2, 0:2] = R
# apply forward transformation (in physical space)
coord_forward_phy = np.array(np.dot((coord_init_phy - np.transpose(centermass_dest_phy)), R3d) + np.transpose(centermass_src_phy))
# apply inverse transformation (in physical space)
coord_inverse_phy = np.array(np.dot((coord_init_phy - np.transpose(centermass_src_phy)), R3d.T) + np.transpose(centermass_dest_phy))
# display rotations
if verbose == 2 and not angle_src_dest[iz] == 0:
# compute new coordinates
coord_src_rot = coord_src[iz] * R
coord_dest_rot = coord_dest[iz] * R.T
# generate figure
plt.figure('iz=' + str(iz) + ', angle_src_dest=' + str(angle_src_dest[iz]), figsize=(9, 9))
# plt.ion() # enables interactive mode (allows keyboard interruption)
# plt.title('iz='+str(iz))
for isub in [221, 222, 223, 224]:
# plt.figure
plt.subplot(isub)
# ax = matplotlib.pyplot.axis()
try:
if isub == 221:
plt.scatter(coord_src[iz][:, 0], coord_src[iz][:, 1], s=5, marker='o', zorder=10, color='steelblue',
alpha=0.5)
pcaaxis = pca_src[iz].components_.T
pca_eigenratio = pca_src[iz].explained_variance_ratio_
plt.title('src')
elif isub == 222:
plt.scatter([coord_src_rot[i, 0] for i in range(len(coord_src_rot))], [coord_src_rot[i, 1] for i in range(len(coord_src_rot))], s=5, marker='o', zorder=10, color='steelblue', alpha=0.5)
pcaaxis = pca_dest[iz].components_.T
pca_eigenratio = pca_dest[iz].explained_variance_ratio_
plt.title('src_rot')
elif isub == 223:
plt.scatter(coord_dest[iz][:, 0], coord_dest[iz][:, 1], s=5, marker='o', zorder=10, color='red',
alpha=0.5)
pcaaxis = pca_dest[iz].components_.T
pca_eigenratio = pca_dest[iz].explained_variance_ratio_
plt.title('dest')
elif isub == 224:
plt.scatter([coord_dest_rot[i, 0] for i in range(len(coord_dest_rot))], [coord_dest_rot[i, 1] for i in range(len(coord_dest_rot))], s=5, marker='o', zorder=10, color='red', alpha=0.5)
pcaaxis = pca_src[iz].components_.T
pca_eigenratio = pca_src[iz].explained_variance_ratio_
plt.title('dest_rot')
plt.text(-2.5, -2, 'eigenvectors:', horizontalalignment='left', verticalalignment='bottom')
plt.text(-2.5, -2.8, str(pcaaxis), horizontalalignment='left', verticalalignment='bottom')
plt.text(-2.5, 2.5, 'eigenval_ratio:', horizontalalignment='left', verticalalignment='bottom')
plt.text(-2.5, 2, str(pca_eigenratio), horizontalalignment='left', verticalalignment='bottom')
plt.plot([0, pcaaxis[0, 0]], [0, pcaaxis[1, 0]], linewidth=2, color='red')
plt.plot([0, pcaaxis[0, 1]], [0, pcaaxis[1, 1]], linewidth=2, color='orange')
plt.axis([-3, 3, -3, 3])
plt.gca().set_aspect('equal', adjustable='box')
except Exception as e:
raise Exception
# sct.printv('Error on line {}'.format(sys.exc_info()[-1].tb_lineno), 1, 'warning')
# sct.printv('WARNING: '+str(e), 1, 'warning')
# plt.axis('equal')
plt.savefig(os.path.join(path_qc, 'register2d_centermassrot_pca_z' + str(iz) + '.png'))
plt.close()
# construct 3D warping matrix
warp_x[:, :, iz] = np.array([coord_forward_phy[i, 0] - coord_init_phy[i, 0] for i in range(nx * ny)]).reshape((nx, ny))
warp_y[:, :, iz] = np.array([coord_forward_phy[i, 1] - coord_init_phy[i, 1] for i in range(nx * ny)]).reshape((nx, ny))
warp_inv_x[:, :, iz] = np.array([coord_inverse_phy[i, 0] - coord_init_phy[i, 0] for i in range(nx * ny)]).reshape((nx, ny))
warp_inv_y[:, :, iz] = np.array([coord_inverse_phy[i, 1] - coord_init_phy[i, 1] for i in range(nx * ny)]).reshape((nx, ny))
sct.log.info('\n Done')
# Generate forward warping field (defined in destination space)
generate_warping_field(fname_dest, warp_x, warp_y, fname_warp, verbose)
generate_warping_field(fname_src, warp_inv_x, warp_inv_y, fname_warp_inv, verbose)
def fmri_moco(param):
file_data = "fmri.nii"
mat_final = 'mat_final/'
ext_mat = 'Warp.nii.gz' # warping field
# Get dimensions of data
sct.printv('\nGet dimensions of data...', param.verbose)
im_data = Image(param.fname_data)
nx, ny, nz, nt, px, py, pz, pt = im_data.dim
sct.printv(' ' + str(nx) + ' x ' + str(ny) + ' x ' + str(nz) + ' x ' + str(nt), param.verbose)
# Get orientation
sct.printv('\nData orientation: ' + im_data.orientation, param.verbose)
if im_data.orientation[2] in 'LR':
param.is_sagittal = True
sct.printv(' Treated as sagittal')
elif im_data.orientation[2] in 'IS':
param.is_sagittal = False
sct.printv(' Treated as axial')
else:
param.is_sagittal = False
sct.printv('WARNING: Orientation seems to be neither axial nor sagittal.')
# Adjust group size in case of sagittal scan
if param.is_sagittal and param.group_size != 1:
sct.printv('For sagittal data group_size should be one for more robustness. Forcing group_size=1.', 1, 'warning')
param.group_size = 1
# Split into T dimension
sct.printv('\nSplit along T dimension...', param.verbose)
im_data_split_list = split_data(im_data, 3)
for im in im_data_split_list:
x_dirname, x_basename, x_ext = sct.extract_fname(im.absolutepath)
# Make further steps slurp the data to avoid too many open files (#2149)
im.absolutepath = os.path.join(x_dirname, x_basename + ".nii.gz")
im.save()
# assign an index to each volume
index_fmri = list(range(0, nt))
# Number of groups
nb_groups = int(math.floor(nt / param.group_size))
# Generate groups indexes
group_indexes = []
for iGroup in range(nb_groups):
group_indexes.append(index_fmri[(iGroup * param.group_size):((iGroup + 1) * param.group_size)])
# add the remaining images to the last fMRI group
nb_remaining = nt%param.group_size # number of remaining images
if nb_remaining > 0:
nb_groups += 1
group_indexes.append(index_fmri[len(index_fmri) - nb_remaining:len(index_fmri)])
# groups
for iGroup in tqdm(range(nb_groups), unit='iter', unit_scale=False, desc="Merge within groups", ascii=True, ncols=80):
# get index
index_fmri_i = group_indexes[iGroup]
nt_i = len(index_fmri_i)
# Merge Images
file_data_merge_i = sct.add_suffix(file_data, '_' + str(iGroup))
# cmd = fsloutput + 'fslmerge -t ' + file_data_merge_i
# for it in range(nt_i):
# cmd = cmd + ' ' + file_data + '_T' + str(index_fmri_i[it]).zfill(4)
im_fmri_list = []
for it in range(nt_i):
im_fmri_list.append(im_data_split_list[index_fmri_i[it]])
im_fmri_concat = concat_data(im_fmri_list, 3, squeeze_data=True).save(file_data_merge_i)
file_data_mean = sct.add_suffix(file_data, '_mean_' + str(iGroup))
if file_data_mean.endswith(".nii"):
file_data_mean += ".gz" # #2149
if param.group_size == 1:
# copy to new file name instead of averaging (faster)
# note: this is a bandage. Ideally we should skip this entire for loop if g=1
convert(file_data_merge_i, file_data_mean)
else:
# Average Images
sct.run(['sct_maths', '-i', file_data_merge_i, '-o', file_data_mean, '-mean', 't'], verbose=0)
# if not average_data_across_dimension(file_data_merge_i+'.nii', file_data_mean+'.nii', 3):
# sct.printv('ERROR in average_data_across_dimension', 1, 'error')
# cmd = fsloutput + 'fslmaths ' + file_data_merge_i + ' -Tmean ' + file_data_mean
# sct.run(cmd, param.verbose)
# Merge groups means. The output 4D volume will be used for motion correction.
sct.printv('\nMerging volumes...', param.verbose)
file_data_groups_means_merge = 'fmri_averaged_groups.nii'
im_mean_list = []
for iGroup in range(nb_groups):
file_data_mean = sct.add_suffix(file_data, '_mean_' + str(iGroup))
if file_data_mean.endswith(".nii"):
file_data_mean += ".gz" # #2149
im_mean_list.append(Image(file_data_mean))
im_mean_concat = concat_data(im_mean_list, 3).save(file_data_groups_means_merge)
# Estimate moco
sct.printv('\n-------------------------------------------------------------------------------', param.verbose)
sct.printv(' Estimating motion...', param.verbose)
sct.printv('-------------------------------------------------------------------------------', param.verbose)
param_moco = param
param_moco.file_data = 'fmri_averaged_groups.nii'
param_moco.file_target = sct.add_suffix(file_data, '_mean_' + param.num_target)
if param_moco.file_target.endswith(".nii"):
param_moco.file_target += ".gz" # #2149
param_moco.path_out = ''
param_moco.todo = 'estimate_and_apply'
param_moco.mat_moco = 'mat_groups'
file_mat = moco.moco(param_moco)
# TODO: if g=1, no need to run the block below (already applied)
if param.group_size == 1:
# if flag g=1, it means that all images have already been corrected, so we just need to rename the file
sct.mv('fmri_averaged_groups_moco.nii', 'fmri_moco.nii')
else:
# create final mat folder
sct.create_folder(mat_final)
# Copy registration matrices
sct.printv('\nCopy transformations...', param.verbose)
for iGroup in range(nb_groups):
for data in range(len(group_indexes[iGroup])): # we cannot use enumerate because group_indexes has 2 dim.
# fetch all file_mat_z for given t-group
list_file_mat_z = file_mat[:, iGroup]
# loop across file_mat_z and copy to mat_final folder
for file_mat_z in list_file_mat_z:
# we want to copy 'mat_groups/mat.ZXXXXTYYYYWarp.nii.gz' --> 'mat_final/mat.ZXXXXTYYYZWarp.nii.gz'
# Notice the Y->Z in the under the T index: the idea here is to use the single matrix from each group,
# and apply it to all images belonging to the same group.
sct.copy(file_mat_z + ext_mat,
mat_final + file_mat_z[11:20] + 'T' + str(group_indexes[iGroup][data]).zfill(4) + ext_mat)
# Apply moco on all fmri data
sct.printv('\n-------------------------------------------------------------------------------', param.verbose)
sct.printv(' Apply moco', param.verbose)
sct.printv('-------------------------------------------------------------------------------', param.verbose)
param_moco.file_data = 'fmri.nii'
param_moco.file_target = sct.add_suffix(file_data, '_mean_' + str(0))
if param_moco.file_target.endswith(".nii"):
param_moco.file_target += ".gz"
param_moco.path_out = ''
param_moco.mat_moco = mat_final
param_moco.todo = 'apply'
moco.moco(param_moco)
# copy geometric information from header
# NB: this is required because WarpImageMultiTransform in 2D mode wrongly sets pixdim(3) to "1".
im_fmri = Image('fmri.nii')
im_fmri_moco = Image('fmri_moco.nii')
im_fmri_moco.header = im_fmri.header
im_fmri_moco.save()
# Average volumes
sct.printv('\nAveraging data...', param.verbose)
sct_maths.main(args=['-i', 'fmri_moco.nii',
'-o', 'fmri_moco_mean.nii',
'-mean', 't',
'-v', '0'])
def moco(param):
# retrieve parameters
file_data = param.file_data
file_target = param.file_target
folder_mat = param.mat_moco # output folder of mat file
todo = param.todo
suffix = param.suffix
verbose = param.verbose
# other parameters
file_mask = 'mask.nii'
sct.printv('\nInput parameters:', param.verbose)
sct.printv(' Input file ............' + file_data, param.verbose)
sct.printv(' Reference file ........' + file_target, param.verbose)
sct.printv(' Polynomial degree .....' + param.poly, param.verbose)
sct.printv(' Smoothing kernel ......' + param.smooth, param.verbose)
sct.printv(' Gradient step .........' + param.gradStep, param.verbose)
sct.printv(' Metric ................' + param.metric, param.verbose)
sct.printv(' Sampling ..............' + param.sampling, param.verbose)
sct.printv(' Todo ..................' + todo, param.verbose)
sct.printv(' Mask .................' + param.fname_mask, param.verbose)
sct.printv(' Output mat folder .....' + folder_mat, param.verbose)
# create folder for mat files
sct.create_folder(folder_mat)
# Get size of data
sct.printv('\nData dimensions:', verbose)
im_data = Image(param.file_data)
nx, ny, nz, nt, px, py, pz, pt = im_data.dim
sct.printv((' ' + str(nx) + ' x ' + str(ny) + ' x ' + str(nz) + ' x ' + str(nt)), verbose)
# copy file_target to a temporary file
sct.printv('\nCopy file_target to a temporary file...', verbose)
file_target = "target.nii.gz"
convert(param.file_target, file_target)
# If scan is sagittal, split src and target along Z (slice)
if param.is_sagittal:
dim_sag = 2 # TODO: find it
# z-split data (time series)
im_z_list = split_data(im_data, dim=dim_sag, squeeze_data=False)
file_data_splitZ = []
for im_z in im_z_list:
im_z.save()
file_data_splitZ.append(im_z.absolutepath)
# z-split target
im_targetz_list = split_data(Image(file_target), dim=dim_sag, squeeze_data=False)
file_target_splitZ = []
for im_targetz in im_targetz_list:
im_targetz.save()
file_target_splitZ.append(im_targetz.absolutepath)
# z-split mask (if exists)
if not param.fname_mask == '':
im_maskz_list = split_data(Image(file_mask), dim=dim_sag, squeeze_data=False)
file_mask_splitZ = []
for im_maskz in im_maskz_list:
im_maskz.save()
file_mask_splitZ.append(im_maskz.absolutepath)
# initialize file list for output matrices
file_mat = np.empty((nz, nt), dtype=object)
# axial orientation
else:
file_data_splitZ = [file_data] # TODO: make it absolute like above
file_target_splitZ = [file_target] # TODO: make it absolute like above
# initialize file list for output matrices
file_mat = np.empty((1, nt), dtype=object)
# deal with mask
if not param.fname_mask == '':
convert(param.fname_mask, file_mask, squeeze_data=False)
im_maskz_list = [Image(file_mask)] # use a list with single element
# Loop across file list, where each file is either a 2D volume (if sagittal) or a 3D volume (otherwise)
# file_mat = tuple([[[] for i in range(nt)] for i in range(nz)])
file_data_splitZ_moco = []
sct.printv('\nRegister. Loop across Z (note: there is only one Z if orientation is axial')
for file in file_data_splitZ:
iz = file_data_splitZ.index(file)
# Split data along T dimension
# sct.printv('\nSplit data along T dimension.', verbose)
im_z = Image(file)
list_im_zt = split_data(im_z, dim=3)
file_data_splitZ_splitT = []
for im_zt in list_im_zt:
im_zt.save(verbose=0)
file_data_splitZ_splitT.append(im_zt.absolutepath)
# file_data_splitT = file_data + '_T'
# Motion correction: initialization
index = np.arange(nt)
file_data_splitT_num = []
file_data_splitZ_splitT_moco = []
failed_transfo = [0 for i in range(nt)]
# Motion correction: Loop across T
for indice_index in tqdm(range(nt), unit='iter', unit_scale=False,
desc="Z=" + str(iz) + "/" + str(len(file_data_splitZ)-1), ascii=True, ncols=80):
# create indices and display stuff
it = index[indice_index]
file_mat[iz][it] = os.path.join(folder_mat, "mat.Z") + str(iz).zfill(4) + 'T' + str(it).zfill(4)
file_data_splitZ_splitT_moco.append(sct.add_suffix(file_data_splitZ_splitT[it], '_moco'))
# deal with masking
if not param.fname_mask == '':
input_mask = im_maskz_list[iz]
else:
input_mask = None
# run 3D registration
failed_transfo[it] = register(param, file_data_splitZ_splitT[it], file_target_splitZ[iz], file_mat[iz][it],
file_data_splitZ_splitT_moco[it], im_mask=input_mask)
# average registered volume with target image
# N.B. use weighted averaging: (target * nb_it + moco) / (nb_it + 1)
if param.iterAvg and indice_index < 10 and failed_transfo[it] == 0 and not param.todo == 'apply':
im_targetz = Image(file_target_splitZ[iz])
data_targetz = im_targetz.data
data_mocoz = Image(file_data_splitZ_splitT_moco[it]).data
data_targetz = (data_targetz * (indice_index + 1) + data_mocoz) / (indice_index + 2)
im_targetz.data = data_targetz
im_targetz.save(verbose=0)
# Replace failed transformation with the closest good one
fT = [i for i, j in enumerate(failed_transfo) if j == 1]
gT = [i for i, j in enumerate(failed_transfo) if j == 0]
for it in range(len(fT)):
abs_dist = [np.abs(gT[i] - fT[it]) for i in range(len(gT))]
if not abs_dist == []:
index_good = abs_dist.index(min(abs_dist))
sct.printv(' transfo #' + str(fT[it]) + ' --> use transfo #' + str(gT[index_good]), verbose)
# copy transformation
sct.copy(file_mat[iz][gT[index_good]] + 'Warp.nii.gz', file_mat[iz][fT[it]] + 'Warp.nii.gz')
# apply transformation
sct_apply_transfo.main(args=['-i', file_data_splitZ_splitT[fT[it]],
'-d', file_target,
'-w', file_mat[iz][fT[it]] + 'Warp.nii.gz',
'-o', file_data_splitZ_splitT_moco[fT[it]],
'-x', param.interp])
else:
# exit program if no transformation exists.
sct.printv('\nERROR in ' + os.path.basename(__file__) + ': No good transformation exist. Exit program.\n', verbose, 'error')
sys.exit(2)
# Merge data along T
file_data_splitZ_moco.append(sct.add_suffix(file, suffix))
if todo != 'estimate':
im_out = concat_data(file_data_splitZ_splitT_moco, 3)
im_out.save(file_data_splitZ_moco[iz])
# If sagittal, merge along Z
if param.is_sagittal:
im_out = concat_data(file_data_splitZ_moco, 2)
dirname, basename, ext = sct.extract_fname(file_data)
path_out = os.path.join(dirname, basename + suffix + ext)
im_out.save(path_out)
return file_mat
def moco_wrapper(param):
"""
Wrapper that performs motion correction.
:param param: ParamMoco class
:return: None
"""
file_data = 'data.nii' # corresponds to the full input data (e.g. dmri or fmri)
file_data_dirname, file_data_basename, file_data_ext = extract_fname(
file_data)
file_b0 = 'b0.nii'
file_datasub = 'datasub.nii' # corresponds to the full input data minus the b=0 scans (if param.is_diffusion=True)
file_datasubgroup = 'datasub-groups.nii' # concatenation of the average of each file_datasub
file_mask = 'mask.nii'
file_moco_params_csv = 'moco_params.tsv'
file_moco_params_x = 'moco_params_x.nii.gz'
file_moco_params_y = 'moco_params_y.nii.gz'
ext_data = '.nii.gz' # workaround "too many open files" by slurping the data
# TODO: check if .nii can be used
mat_final = 'mat_final/'
# ext_mat = 'Warp.nii.gz' # warping field
# Start timer
start_time = time.time()
printv('\nInput parameters:', param.verbose)
printv(' Input file ............ ' + param.fname_data, param.verbose)
printv(' Group size ............ {}'.format(param.group_size),
param.verbose)
# Get full path
# param.fname_data = os.path.abspath(param.fname_data)
# param.fname_bvecs = os.path.abspath(param.fname_bvecs)
# if param.fname_bvals != '':
# param.fname_bvals = os.path.abspath(param.fname_bvals)
# Extract path, file and extension
# path_data, file_data, ext_data = extract_fname(param.fname_data)
# path_mask, file_mask, ext_mask = extract_fname(param.fname_mask)
path_tmp = tmp_create(basename="moco")
# Copying input data to tmp folder
printv('\nCopying input data to tmp folder and convert to nii...',
param.verbose)
convert(param.fname_data, os.path.join(path_tmp, file_data))
if param.fname_mask != '':
convert(param.fname_mask,
os.path.join(path_tmp, file_mask),
verbose=param.verbose)
# Update field in param (because used later in another function, and param class will be passed)
param.fname_mask = file_mask
# Build absolute output path and go to tmp folder
curdir = os.getcwd()
path_out_abs = os.path.abspath(param.path_out)
os.chdir(path_tmp)
# Get dimensions of data
printv('\nGet dimensions of data...', param.verbose)
im_data = Image(file_data)
nx, ny, nz, nt, px, py, pz, pt = im_data.dim
printv(' ' + str(nx) + ' x ' + str(ny) + ' x ' + str(nz), param.verbose)
# Get orientation
printv('\nData orientation: ' + im_data.orientation, param.verbose)
if im_data.orientation[2] in 'LR':
param.is_sagittal = True
printv(' Treated as sagittal')
elif im_data.orientation[2] in 'IS':
param.is_sagittal = False
printv(' Treated as axial')
else:
param.is_sagittal = False
printv(
'WARNING: Orientation seems to be neither axial nor sagittal. Treated as axial.'
)
printv(
"\nSet suffix of transformation file name, which depends on the orientation:"
)
if param.is_sagittal:
param.suffix_mat = '0GenericAffine.mat'
printv(
"Orientation is sagittal, suffix is '{}'. The image is split across the R-L direction, and the "
"estimated transformation is a 2D affine transfo.".format(
param.suffix_mat))
else:
param.suffix_mat = 'Warp.nii.gz'
printv(
"Orientation is axial, suffix is '{}'. The estimated transformation is a 3D warping field, which is "
"composed of a stack of 2D Tx-Ty transformations".format(
param.suffix_mat))
# Adjust group size in case of sagittal scan
if param.is_sagittal and param.group_size != 1:
printv(
'For sagittal data group_size should be one for more robustness. Forcing group_size=1.',
1, 'warning')
param.group_size = 1
if param.is_diffusion:
# Identify b=0 and DWI images
index_b0, index_dwi, nb_b0, nb_dwi = \
sct_dmri_separate_b0_and_dwi.identify_b0(param.fname_bvecs, param.fname_bvals, param.bval_min,
param.verbose)
# check if dmri and bvecs are the same size
if not nb_b0 + nb_dwi == nt:
printv(
'\nERROR in ' + os.path.basename(__file__) +
': Size of data (' + str(nt) + ') and size of bvecs (' +
str(nb_b0 + nb_dwi) +
') are not the same. Check your bvecs file.\n', 1, 'error')
sys.exit(2)
# ==================================================================================================================
# Prepare data (mean/groups...)
# ==================================================================================================================
# Split into T dimension
printv('\nSplit along T dimension...', param.verbose)
im_data_split_list = split_data(im_data, 3)
for im in im_data_split_list:
x_dirname, x_basename, x_ext = extract_fname(im.absolutepath)
im.absolutepath = os.path.join(x_dirname, x_basename + ".nii.gz")
im.save()
if param.is_diffusion:
# Merge and average b=0 images
printv('\nMerge and average b=0 data...', param.verbose)
im_b0_list = []
for it in range(nb_b0):
im_b0_list.append(im_data_split_list[index_b0[it]])
im_b0 = concat_data(im_b0_list, 3).save(file_b0, verbose=0)
# Average across time
im_b0.mean(dim=3).save(add_suffix(file_b0, '_mean'))
n_moco = nb_dwi # set number of data to perform moco on (using grouping)
index_moco = index_dwi
# If not a diffusion scan, we will motion-correct all volumes
else:
n_moco = nt
index_moco = list(range(0, nt))
nb_groups = int(math.floor(n_moco / param.group_size))
# Generate groups indexes
group_indexes = []
for iGroup in range(nb_groups):
group_indexes.append(index_moco[(iGroup *
param.group_size):((iGroup + 1) *
param.group_size)])
# add the remaining images to a new last group (in case the total number of image is not divisible by group_size)
nb_remaining = n_moco % param.group_size # number of remaining images
if nb_remaining > 0:
nb_groups += 1
group_indexes.append(index_moco[len(index_moco) -
nb_remaining:len(index_moco)])
_, file_dwi_basename, file_dwi_ext = extract_fname(file_datasub)
# Group data
list_file_group = []
for iGroup in sct_progress_bar(range(nb_groups),
unit='iter',
unit_scale=False,
desc="Merge within groups",
ascii=False,
ncols=80):
# get index
index_moco_i = group_indexes[iGroup]
n_moco_i = len(index_moco_i)
# concatenate images across time, within this group
file_dwi_merge_i = os.path.join(file_dwi_basename + '_' + str(iGroup) +
ext_data)
im_dwi_list = []
for it in range(n_moco_i):
im_dwi_list.append(im_data_split_list[index_moco_i[it]])
im_dwi_out = concat_data(im_dwi_list, 3).save(file_dwi_merge_i,
verbose=0)
# Average across time
list_file_group.append(
os.path.join(file_dwi_basename + '_' + str(iGroup) + '_mean' +
ext_data))
im_dwi_out.mean(dim=3).save(list_file_group[-1])
# Merge across groups
printv('\nMerge across groups...', param.verbose)
# file_dwi_groups_means_merge = 'dwi_averaged_groups'
im_dw_list = []
for iGroup in range(nb_groups):
im_dw_list.append(list_file_group[iGroup])
concat_data(im_dw_list, 3).save(file_datasubgroup, verbose=0)
# Cleanup
del im, im_data_split_list
# ==================================================================================================================
# Estimate moco
# ==================================================================================================================
# Initialize another class instance that will be passed on to the moco() function
param_moco = deepcopy(param)
if param.is_diffusion:
# Estimate moco on b0 groups
printv(
'\n-------------------------------------------------------------------------------',
param.verbose)
printv(' Estimating motion on b=0 images...', param.verbose)
printv(
'-------------------------------------------------------------------------------',
param.verbose)
param_moco.file_data = 'b0.nii'
# Identify target image
if index_moco[0] != 0:
# If first DWI is not the first volume (most common), then there is a least one b=0 image before. In that
# case select it as the target image for registration of all b=0
param_moco.file_target = os.path.join(
file_data_dirname, file_data_basename + '_T' +
str(index_b0[index_moco[0] - 1]).zfill(4) + ext_data)
else:
# If first DWI is the first volume, then the target b=0 is the first b=0 from the index_b0.
param_moco.file_target = os.path.join(
file_data_dirname, file_data_basename + '_T' +
str(index_b0[0]).zfill(4) + ext_data)
# Run moco
param_moco.path_out = ''
param_moco.todo = 'estimate_and_apply'
param_moco.mat_moco = 'mat_b0groups'
file_mat_b0, _ = moco(param_moco)
# Estimate moco across groups
printv(
'\n-------------------------------------------------------------------------------',
param.verbose)
printv(' Estimating motion across groups...', param.verbose)
printv(
'-------------------------------------------------------------------------------',
param.verbose)
param_moco.file_data = file_datasubgroup
param_moco.file_target = list_file_group[
0] # target is the first volume (closest to the first b=0 if DWI scan)
param_moco.path_out = ''
param_moco.todo = 'estimate_and_apply'
param_moco.mat_moco = 'mat_groups'
file_mat_datasub_group, _ = moco(param_moco)
# Spline Regularization along T
if param.spline_fitting:
# TODO: fix this scenario (haven't touched that code for a while-- it is probably buggy)
raise NotImplementedError()
# spline(mat_final, nt, nz, param.verbose, np.array(index_b0), param.plot_graph)
# ==================================================================================================================
# Apply moco
# ==================================================================================================================
# If group_size>1, assign transformation to each individual ungrouped 3d volume
if param.group_size > 1:
file_mat_datasub = []
for iz in range(len(file_mat_datasub_group)):
# duplicate by factor group_size the transformation file for each it
# example: [mat.Z0000T0001Warp.nii] --> [mat.Z0000T0001Warp.nii, mat.Z0000T0001Warp.nii] for group_size=2
file_mat_datasub.append(
functools.reduce(operator.iconcat,
[[i] * param.group_size
for i in file_mat_datasub_group[iz]], []))
else:
file_mat_datasub = file_mat_datasub_group
# Copy transformations to mat_final folder and rename them appropriately
copy_mat_files(nt, file_mat_datasub, index_moco, mat_final, param)
if param.is_diffusion:
copy_mat_files(nt, file_mat_b0, index_b0, mat_final, param)
# Apply moco on all dmri data
printv(
'\n-------------------------------------------------------------------------------',
param.verbose)
printv(' Apply moco', param.verbose)
printv(
'-------------------------------------------------------------------------------',
param.verbose)
param_moco.file_data = file_data
param_moco.file_target = list_file_group[
0] # reference for reslicing into proper coordinate system
param_moco.path_out = '' # TODO not used in moco()
param_moco.mat_moco = mat_final
param_moco.todo = 'apply'
file_mat_data, im_moco = moco(param_moco)
# copy geometric information from header
# NB: this is required because WarpImageMultiTransform in 2D mode wrongly sets pixdim(3) to "1".
im_moco.header = im_data.header
im_moco.save(verbose=0)
# Average across time
if param.is_diffusion:
# generate b0_moco_mean and dwi_moco_mean
args = [
'-i', im_moco.absolutepath, '-bvec', param.fname_bvecs, '-a', '1',
'-v', '0'
]
if not param.fname_bvals == '':
# if bvals file is provided
args += ['-bval', param.fname_bvals]
fname_b0, fname_b0_mean, fname_dwi, fname_dwi_mean = sct_dmri_separate_b0_and_dwi.main(
args=args)
else:
fname_moco_mean = add_suffix(im_moco.absolutepath, '_mean')
im_moco.mean(dim=3).save(fname_moco_mean)
# Extract and output the motion parameters (doesn't work for sagittal orientation)
printv('Extract motion parameters...')
if param.output_motion_param:
if param.is_sagittal:
printv(
'Motion parameters cannot be generated for sagittal images.',
1, 'warning')
else:
files_warp_X, files_warp_Y = [], []
moco_param = []
for fname_warp in file_mat_data[0]:
# Cropping the image to keep only one voxel in the XY plane
im_warp = Image(fname_warp + param.suffix_mat)
im_warp.data = np.expand_dims(np.expand_dims(
im_warp.data[0, 0, :, :, :], axis=0),
axis=0)
# These three lines allow to generate one file instead of two, containing X, Y and Z moco parameters
#fname_warp_crop = fname_warp + '_crop_' + ext_mat
# files_warp.append(fname_warp_crop)
# im_warp.save(fname_warp_crop)
# Separating the three components and saving X and Y only (Z is equal to 0 by default).
im_warp_XYZ = multicomponent_split(im_warp)
fname_warp_crop_X = fname_warp + '_crop_X_' + param.suffix_mat
im_warp_XYZ[0].save(fname_warp_crop_X)
files_warp_X.append(fname_warp_crop_X)
fname_warp_crop_Y = fname_warp + '_crop_Y_' + param.suffix_mat
im_warp_XYZ[1].save(fname_warp_crop_Y)
files_warp_Y.append(fname_warp_crop_Y)
# Calculating the slice-wise average moco estimate to provide a QC file
moco_param.append([
np.mean(np.ravel(im_warp_XYZ[0].data)),
np.mean(np.ravel(im_warp_XYZ[1].data))
])
# These two lines allow to generate one file instead of two, containing X, Y and Z moco parameters
#im_warp_concat = concat_data(files_warp, dim=3)
# im_warp_concat.save('fmri_moco_params.nii')
# Concatenating the moco parameters into a time series for X and Y components.
im_warp_concat = concat_data(files_warp_X, dim=3)
im_warp_concat.save(file_moco_params_x)
im_warp_concat = concat_data(files_warp_Y, dim=3)
im_warp_concat.save(file_moco_params_y)
# Writing a TSV file with the slicewise average estimate of the moco parameters. Useful for QC
with open(file_moco_params_csv, 'wt') as out_file:
tsv_writer = csv.writer(out_file, delimiter='\t')
tsv_writer.writerow(['X', 'Y'])
for mocop in moco_param:
tsv_writer.writerow([mocop[0], mocop[1]])
# Generate output files
printv('\nGenerate output files...', param.verbose)
fname_moco = os.path.join(
path_out_abs,
add_suffix(os.path.basename(param.fname_data), param.suffix))
generate_output_file(im_moco.absolutepath, fname_moco)
if param.is_diffusion:
generate_output_file(fname_b0_mean, add_suffix(fname_moco, '_b0_mean'))
generate_output_file(fname_dwi_mean,
add_suffix(fname_moco, '_dwi_mean'))
else:
generate_output_file(fname_moco_mean, add_suffix(fname_moco, '_mean'))
if os.path.exists(file_moco_params_csv):
generate_output_file(file_moco_params_x,
os.path.join(path_out_abs, file_moco_params_x),
squeeze_data=False)
generate_output_file(file_moco_params_y,
os.path.join(path_out_abs, file_moco_params_y),
squeeze_data=False)
generate_output_file(file_moco_params_csv,
os.path.join(path_out_abs, file_moco_params_csv))
# Delete temporary files
if param.remove_temp_files == 1:
printv('\nDelete temporary files...', param.verbose)
rmtree(path_tmp, verbose=param.verbose)
# come back to working directory
os.chdir(curdir)
# display elapsed time
elapsed_time = time.time() - start_time
printv(
'\nFinished! Elapsed time: ' + str(int(np.round(elapsed_time))) + 's',
param.verbose)
display_viewer_syntax([
os.path.join(
param.path_out,
add_suffix(os.path.basename(param.fname_data), param.suffix)),
param.fname_data
],
mode='ortho,ortho')
def fmri_moco(param):
file_data = 'fmri'
ext_data = '.nii'
mat_final = 'mat_final/'
fsloutput = 'export FSLOUTPUTTYPE=NIFTI; ' # for faster processing, all outputs are in NIFTI
ext_mat = 'Warp.nii.gz' # warping field
# Get dimensions of data
sct.printv('\nGet dimensions of data...', param.verbose)
nx, ny, nz, nt, px, py, pz, pt = Image(file_data+'.nii').dim
sct.printv(' ' + str(nx) + ' x ' + str(ny) + ' x ' + str(nz) + ' x ' + str(nt), param.verbose)
# Split into T dimension
sct.printv('\nSplit along T dimension...', param.verbose)
im_data = Image(file_data + ext_data)
im_data_split_list = split_data(im_data, 3)
for im in im_data_split_list:
im.save()
# assign an index to each volume
index_fmri = range(0, nt)
# Number of groups
nb_groups = int(math.floor(nt/param.group_size))
# Generate groups indexes
group_indexes = []
for iGroup in range(nb_groups):
group_indexes.append(index_fmri[(iGroup*param.group_size):((iGroup+1)*param.group_size)])
# add the remaining images to the last DWI group
nb_remaining = nt%param.group_size # number of remaining images
if nb_remaining > 0:
nb_groups += 1
group_indexes.append(index_fmri[len(index_fmri)-nb_remaining:len(index_fmri)])
# groups
for iGroup in range(nb_groups):
sct.printv('\nGroup: ' +str((iGroup+1))+'/'+str(nb_groups), param.verbose)
# get index
index_fmri_i = group_indexes[iGroup]
nt_i = len(index_fmri_i)
# Merge Images
sct.printv('Merge consecutive volumes...', param.verbose)
file_data_merge_i = file_data + '_' + str(iGroup)
# cmd = fsloutput + 'fslmerge -t ' + file_data_merge_i
# for it in range(nt_i):
# cmd = cmd + ' ' + file_data + '_T' + str(index_fmri_i[it]).zfill(4)
im_fmri_list = []
for it in range(nt_i):
im_fmri_list.append(im_data_split_list[index_fmri_i[it]])
im_fmri_concat = concat_data(im_fmri_list, 3)
im_fmri_concat.setFileName(file_data_merge_i + ext_data)
im_fmri_concat.save()
# Average Images
sct.printv('Average volumes...', param.verbose)
file_data_mean = file_data + '_mean_' + str(iGroup)
sct.run('sct_maths -i '+file_data_merge_i+'.nii -o '+file_data_mean+'.nii -mean t')
# if not average_data_across_dimension(file_data_merge_i+'.nii', file_data_mean+'.nii', 3):
# sct.printv('ERROR in average_data_across_dimension', 1, 'error')
# cmd = fsloutput + 'fslmaths ' + file_data_merge_i + ' -Tmean ' + file_data_mean
# sct.run(cmd, param.verbose)
# Merge groups means
sct.printv('\nMerging volumes...', param.verbose)
file_data_groups_means_merge = 'fmri_averaged_groups'
# cmd = fsloutput + 'fslmerge -t ' + file_data_groups_means_merge
# for iGroup in range(nb_groups):
# cmd = cmd + ' ' + file_data + '_mean_' + str(iGroup)
im_mean_list = []
for iGroup in range(nb_groups):
im_mean_list.append(Image(file_data + '_mean_' + str(iGroup) + ext_data))
im_mean_concat = concat_data(im_mean_list, 3)
im_mean_concat.setFileName(file_data_groups_means_merge + ext_data)
im_mean_concat.save()
# Estimate moco on dwi groups
sct.printv('\n-------------------------------------------------------------------------------', param.verbose)
sct.printv(' Estimating motion...', param.verbose)
sct.printv('-------------------------------------------------------------------------------', param.verbose)
param_moco = param
param_moco.file_data = 'fmri_averaged_groups'
param_moco.file_target = file_data + '_mean_' + str(param.num_target)
param_moco.path_out = ''
param_moco.todo = 'estimate_and_apply'
param_moco.mat_moco = 'mat_groups'
moco.moco(param_moco)
# create final mat folder
sct.create_folder(mat_final)
# Copy registration matrices
sct.printv('\nCopy transformations...', param.verbose)
for iGroup in range(nb_groups):
for data in range(len(group_indexes[iGroup])):
# if param.slicewise:
# for iz in range(nz):
# sct.run('cp '+'mat_dwigroups/'+'mat.T'+str(iGroup)+'_Z'+str(iz)+ext_mat+' '+mat_final+'mat.T'+str(group_indexes[iGroup][dwi])+'_Z'+str(iz)+ext_mat, param.verbose)
# else:
sct.run('cp '+'mat_groups/'+'mat.T'+str(iGroup)+ext_mat+' '+mat_final+'mat.T'+str(group_indexes[iGroup][data])+ext_mat, param.verbose)
# Apply moco on all fmri data
sct.printv('\n-------------------------------------------------------------------------------', param.verbose)
sct.printv(' Apply moco', param.verbose)
sct.printv('-------------------------------------------------------------------------------', param.verbose)
param_moco.file_data = 'fmri'
param_moco.file_target = file_data+'_mean_'+str(0)
param_moco.path_out = ''
param_moco.mat_moco = mat_final
param_moco.todo = 'apply'
moco.moco(param_moco)
# copy geometric information from header
# NB: this is required because WarpImageMultiTransform in 2D mode wrongly sets pixdim(3) to "1".
im_fmri = Image('fmri.nii')
im_fmri_moco = Image('fmri_moco.nii')
im_fmri_moco = copy_header(im_fmri, im_fmri_moco)
im_fmri_moco.save()
# Average volumes
sct.printv('\nAveraging data...', param.verbose)
sct.run('sct_maths -i fmri_moco.nii -o fmri_moco_mean.nii -mean t')
def find_centerline(algo, image_fname, contrast_type, brain_bool, folder_output, remove_temp_files, centerline_fname):
"""
Assumes RPI orientation
:param algo:
:param image_fname:
:param contrast_type:
:param brain_bool:
:param folder_output:
:param remove_temp_files:
:param centerline_fname:
:return:
"""
# TODO: remove unnecessary i/o
if Image(image_fname).dim[2] == 1: # isct_spine_detect requires nz > 1
im_concat = concat_data([image_fname, image_fname], dim=2)
im_concat.save(sct.add_suffix(image_fname, '_concat'))
image_fname = sct.add_suffix(image_fname, '_concat')
bool_2d = True
else:
bool_2d = False
# TODO: maybe change 'svm' for 'optic', because this is how we call it in sct_get_centerline
if algo == 'svm':
# run optic on a heatmap computed by a trained SVM+HoG algorithm
# optic_models_fname = os.path.join(path_sct, 'data', 'optic_models', '{}_model'.format(contrast_type))
# # TODO: replace with get_centerline(method=optic)
img_ctl, arr_ctl, _ = get_centerline(Image(image_fname), algo_fitting='optic', contrast=contrast_type)
centerline_filename = sct.add_suffix(image_fname, "_ctr")
img_ctl.save(centerline_filename)
elif algo == 'cnn':
# CNN parameters
dct_patch_ctr = {'t2': {'size': (80, 80), 'mean': 51.1417, 'std': 57.4408},
't2s': {'size': (80, 80), 'mean': 68.8591, 'std': 71.4659},
't1': {'size': (80, 80), 'mean': 55.7359, 'std': 64.3149},
'dwi': {'size': (80, 80), 'mean': 55.744, 'std': 45.003}}
dct_params_ctr = {'t2': {'features': 16, 'dilation_layers': 2},
't2s': {'features': 8, 'dilation_layers': 3},
't1': {'features': 24, 'dilation_layers': 3},
'dwi': {'features': 8, 'dilation_layers': 2}}
# load model
ctr_model_fname = os.path.join(sct.__sct_dir__, 'data', 'deepseg_sc_models', '{}_ctr.h5'.format(contrast_type))
ctr_model = nn_architecture_ctr(height=dct_patch_ctr[contrast_type]['size'][0],
width=dct_patch_ctr[contrast_type]['size'][1],
channels=1,
classes=1,
features=dct_params_ctr[contrast_type]['features'],
depth=2,
temperature=1.0,
padding='same',
batchnorm=True,
dropout=0.0,
dilation_layers=dct_params_ctr[contrast_type]['dilation_layers'])
ctr_model.load_weights(ctr_model_fname)
logger.info("Resample the image to 0.5x0.5 mm in-plane resolution...")
fname_res = sct.add_suffix(image_fname, '_resampled')
input_resolution = Image(image_fname).dim[4:7]
new_resolution = 'x'.join(['0.5', '0.5', str(input_resolution[2])])
resampling.resample_file(image_fname, fname_res, new_resolution, 'mm', 'linear', verbose=0)
# compute the heatmap
fname_heatmap = sct.add_suffix(image_fname, "_heatmap")
img_filename = ''.join(sct.extract_fname(fname_heatmap)[:2])
fname_heatmap_nii = img_filename + '.nii'
z_max = heatmap(filename_in=fname_res,
filename_out=fname_heatmap_nii,
model=ctr_model,
patch_shape=dct_patch_ctr[contrast_type]['size'],
mean_train=dct_patch_ctr[contrast_type]['mean'],
std_train=dct_patch_ctr[contrast_type]['std'],
brain_bool=brain_bool)
# run optic on the heatmap
centerline_filename = sct.add_suffix(fname_heatmap, "_ctr")
heatmap2optic(fname_heatmap=fname_heatmap_nii,
lambda_value=7 if contrast_type == 't2s' else 1,
fname_out=centerline_filename,
z_max=z_max if brain_bool else None)
elif algo == 'viewer':
im_labels = _call_viewer_centerline(Image(image_fname))
im_centerline, arr_centerline, _ = get_centerline(im_labels)
centerline_filename = sct.add_suffix(image_fname, "_ctr")
labels_filename = sct.add_suffix(image_fname, "_labels-centerline")
im_centerline.save(centerline_filename)
im_labels.save(labels_filename)
elif algo == 'file':
centerline_filename = sct.add_suffix(image_fname, "_ctr")
# Re-orient the manual centerline
Image(centerline_fname).change_orientation('RPI').save(centerline_filename)
else:
logger.error('The parameter "-centerline" is incorrect. Please try again.')
sys.exit(1)
if algo != 'cnn':
logger.info("Resample the image to 0.5x0.5 mm in-plane resolution...")
fname_res = sct.add_suffix(image_fname, '_resampled')
input_resolution = Image(image_fname).dim[4:7]
new_resolution = 'x'.join(['0.5', '0.5', str(input_resolution[2])])
resampling.resample_file(image_fname, fname_res, new_resolution, 'mm', 'linear', verbose=0)
resampling.resample_file(centerline_filename, centerline_filename, new_resolution, 'mm', 'linear', verbose=0)
if bool_2d:
im_split_lst = split_data(Image(centerline_filename), dim=2)
im_split_lst[0].save(centerline_filename)
return fname_res, centerline_filename
def get_centerline_from_point(input_image, point_file, gap=4, gaussian_kernel=4, remove_tmp_files=1):
# Initialization
fname_anat = input_image
fname_point = point_file
slice_gap = gap
remove_tmp_files = remove_tmp_files
gaussian_kernel = gaussian_kernel
start_time = time()
verbose = 1
# get path of the toolbox
status, path_sct = commands.getstatusoutput("echo $SCT_DIR")
path_sct = sct.slash_at_the_end(path_sct, 1)
# Parameters for debug mode
if param.debug == 1:
sct.printv("\n*** WARNING: DEBUG MODE ON ***\n\t\t\tCurrent working directory: " + os.getcwd(), "warning")
status, path_sct_testing_data = commands.getstatusoutput("echo $SCT_TESTING_DATA_DIR")
fname_anat = path_sct_testing_data + "/t2/t2.nii.gz"
fname_point = path_sct_testing_data + "/t2/t2_centerline_init.nii.gz"
slice_gap = 5
# check existence of input files
sct.check_file_exist(fname_anat)
sct.check_file_exist(fname_point)
# extract path/file/extension
path_anat, file_anat, ext_anat = sct.extract_fname(fname_anat)
path_point, file_point, ext_point = sct.extract_fname(fname_point)
# extract path of schedule file
# TODO: include schedule file in sct
# TODO: check existence of schedule file
file_schedule = path_sct + param.schedule_file
# Get input image orientation
input_image_orientation = get_orientation_3d(fname_anat, filename=True)
# Display arguments
print "\nCheck input arguments..."
print " Anatomical image: " + fname_anat
print " Orientation: " + input_image_orientation
print " Point in spinal cord: " + fname_point
print " Slice gap: " + str(slice_gap)
print " Gaussian kernel: " + str(gaussian_kernel)
print " Degree of polynomial: " + str(param.deg_poly)
# create temporary folder
print ("\nCreate temporary folder...")
path_tmp = "tmp." + strftime("%y%m%d%H%M%S")
sct.create_folder(path_tmp)
print "\nCopy input data..."
sct.run("cp " + fname_anat + " " + path_tmp + "/tmp.anat" + ext_anat)
sct.run("cp " + fname_point + " " + path_tmp + "/tmp.point" + ext_point)
# go to temporary folder
os.chdir(path_tmp)
# convert to nii
im_anat = convert("tmp.anat" + ext_anat, "tmp.anat.nii")
im_point = convert("tmp.point" + ext_point, "tmp.point.nii")
# Reorient input anatomical volume into RL PA IS orientation
print "\nReorient input volume to RL PA IS orientation..."
set_orientation(im_anat, "RPI")
im_anat.setFileName("tmp.anat_orient.nii")
# Reorient binary point into RL PA IS orientation
print "\nReorient binary point into RL PA IS orientation..."
# sct.run(sct.fsloutput + 'fslswapdim tmp.point RL PA IS tmp.point_orient')
set_orientation(im_point, "RPI")
im_point.setFileName("tmp.point_orient.nii")
# Get image dimensions
print "\nGet image dimensions..."
nx, ny, nz, nt, px, py, pz, pt = Image("tmp.anat_orient.nii").dim
print ".. matrix size: " + str(nx) + " x " + str(ny) + " x " + str(nz)
print ".. voxel size: " + str(px) + "mm x " + str(py) + "mm x " + str(pz) + "mm"
# Split input volume
print "\nSplit input volume..."
im_anat_split_list = split_data(im_anat, 2)
file_anat_split = []
for im in im_anat_split_list:
file_anat_split.append(im.absolutepath)
im.save()
im_point_split_list = split_data(im_point, 2)
file_point_split = []
for im in im_point_split_list:
file_point_split.append(im.absolutepath)
im.save()
# Extract coordinates of input point
data_point = Image("tmp.point_orient.nii").data
x_init, y_init, z_init = unravel_index(data_point.argmax(), data_point.shape)
sct.printv("Coordinates of input point: (" + str(x_init) + ", " + str(y_init) + ", " + str(z_init) + ")", verbose)
# Create 2D gaussian mask
sct.printv("\nCreate gaussian mask from point...", verbose)
xx, yy = mgrid[:nx, :ny]
mask2d = zeros((nx, ny))
radius = round(float(gaussian_kernel + 1) / 2) # add 1 because the radius includes the center.
sigma = float(radius)
mask2d = exp(-(((xx - x_init) ** 2) / (2 * (sigma ** 2)) + ((yy - y_init) ** 2) / (2 * (sigma ** 2))))
# Save mask to 2d file
file_mask_split = ["tmp.mask_orient_Z" + str(z).zfill(4) for z in range(0, nz, 1)]
nii_mask2d = Image("tmp.anat_orient_Z0000.nii")
nii_mask2d.data = mask2d
nii_mask2d.setFileName(file_mask_split[z_init] + ".nii")
nii_mask2d.save()
# initialize variables
file_mat = ["tmp.mat_Z" + str(z).zfill(4) for z in range(0, nz, 1)]
file_mat_inv = ["tmp.mat_inv_Z" + str(z).zfill(4) for z in range(0, nz, 1)]
file_mat_inv_cumul = ["tmp.mat_inv_cumul_Z" + str(z).zfill(4) for z in range(0, nz, 1)]
# create identity matrix for initial transformation matrix
fid = open(file_mat_inv_cumul[z_init], "w")
fid.write("%i %i %i %i\n" % (1, 0, 0, 0))
fid.write("%i %i %i %i\n" % (0, 1, 0, 0))
fid.write("%i %i %i %i\n" % (0, 0, 1, 0))
fid.write("%i %i %i %i\n" % (0, 0, 0, 1))
fid.close()
# initialize centerline: give value corresponding to initial point
x_centerline = [x_init]
y_centerline = [y_init]
z_centerline = [z_init]
warning_count = 0
# go up (1), then down (2) in reference to the binary point
for iUpDown in range(1, 3):
if iUpDown == 1:
# z increases
slice_gap_signed = slice_gap
elif iUpDown == 2:
# z decreases
slice_gap_signed = -slice_gap
# reverse centerline (because values will be appended at the end)
x_centerline.reverse()
y_centerline.reverse()
z_centerline.reverse()
# initialization before looping
z_dest = z_init # point given by user
z_src = z_dest + slice_gap_signed
# continue looping if 0 <= z < nz
while 0 <= z_src < nz:
# print current z:
print "z=" + str(z_src) + ":"
# estimate transformation
sct.run(
fsloutput
+ "flirt -in "
+ file_anat_split[z_src]
+ " -ref "
+ file_anat_split[z_dest]
+ " -schedule "
+ file_schedule
+ " -verbose 0 -omat "
+ file_mat[z_src]
+ " -cost normcorr -forcescaling -inweight "
+ file_mask_split[z_dest]
+ " -refweight "
+ file_mask_split[z_dest]
)
# display transfo
status, output = sct.run("cat " + file_mat[z_src])
print output
# check if transformation is bigger than 1.5x slice_gap
tx = float(output.split()[3])
ty = float(output.split()[7])
norm_txy = linalg.norm([tx, ty], ord=2)
if norm_txy > 1.5 * slice_gap:
print "WARNING: Transformation is too large --> using previous one."
warning_count = warning_count + 1
# if previous transformation exists, replace current one with previous one
if os.path.isfile(file_mat[z_dest]):
sct.run("cp " + file_mat[z_dest] + " " + file_mat[z_src])
# estimate inverse transformation matrix
sct.run("convert_xfm -omat " + file_mat_inv[z_src] + " -inverse " + file_mat[z_src])
# compute cumulative transformation
sct.run(
"convert_xfm -omat "
+ file_mat_inv_cumul[z_src]
+ " -concat "
+ file_mat_inv[z_src]
+ " "
+ file_mat_inv_cumul[z_dest]
)
# apply inverse cumulative transformation to initial gaussian mask (to put it in src space)
sct.run(
fsloutput
+ "flirt -in "
+ file_mask_split[z_init]
+ " -ref "
+ file_mask_split[z_init]
+ " -applyxfm -init "
+ file_mat_inv_cumul[z_src]
+ " -out "
+ file_mask_split[z_src]
)
# open inverse cumulative transformation file and generate centerline
fid = open(file_mat_inv_cumul[z_src])
mat = fid.read().split()
x_centerline.append(x_init + float(mat[3]))
y_centerline.append(y_init + float(mat[7]))
z_centerline.append(z_src)
# z_index = z_index+1
# define new z_dest (target slice) and new z_src (moving slice)
z_dest = z_dest + slice_gap_signed
z_src = z_src + slice_gap_signed
# Reconstruct centerline
# ====================================================================================================
# reverse back centerline (because it's been reversed once, so now all values are in the right order)
x_centerline.reverse()
y_centerline.reverse()
z_centerline.reverse()
# fit centerline in the Z-X plane using polynomial function
print "\nFit centerline in the Z-X plane using polynomial function..."
coeffsx = polyfit(z_centerline, x_centerline, deg=param.deg_poly)
polyx = poly1d(coeffsx)
x_centerline_fit = polyval(polyx, z_centerline)
# calculate RMSE
rmse = linalg.norm(x_centerline_fit - x_centerline) / sqrt(len(x_centerline))
# calculate max absolute error
max_abs = max(abs(x_centerline_fit - x_centerline))
print ".. RMSE (in mm): " + str(rmse * px)
print ".. Maximum absolute error (in mm): " + str(max_abs * px)
# fit centerline in the Z-Y plane using polynomial function
print "\nFit centerline in the Z-Y plane using polynomial function..."
coeffsy = polyfit(z_centerline, y_centerline, deg=param.deg_poly)
polyy = poly1d(coeffsy)
y_centerline_fit = polyval(polyy, z_centerline)
# calculate RMSE
rmse = linalg.norm(y_centerline_fit - y_centerline) / sqrt(len(y_centerline))
# calculate max absolute error
max_abs = max(abs(y_centerline_fit - y_centerline))
print ".. RMSE (in mm): " + str(rmse * py)
print ".. Maximum absolute error (in mm): " + str(max_abs * py)
# display
if param.debug == 1:
import matplotlib.pyplot as plt
plt.figure()
plt.plot(z_centerline, x_centerline, ".", z_centerline, x_centerline_fit, "r")
plt.legend(["Data", "Polynomial Fit"])
plt.title("Z-X plane polynomial interpolation")
plt.show()
plt.figure()
plt.plot(z_centerline, y_centerline, ".", z_centerline, y_centerline_fit, "r")
plt.legend(["Data", "Polynomial Fit"])
plt.title("Z-Y plane polynomial interpolation")
plt.show()
# generate full range z-values for centerline
z_centerline_full = [iz for iz in range(0, nz, 1)]
# calculate X and Y values for the full centerline
x_centerline_fit_full = polyval(polyx, z_centerline_full)
y_centerline_fit_full = polyval(polyy, z_centerline_full)
# Generate fitted transformation matrices and write centerline coordinates in text file
print "\nGenerate fitted transformation matrices and write centerline coordinates in text file..."
file_mat_inv_cumul_fit = ["tmp.mat_inv_cumul_fit_z" + str(z).zfill(4) for z in range(0, nz, 1)]
file_mat_cumul_fit = ["tmp.mat_cumul_fit_z" + str(z).zfill(4) for z in range(0, nz, 1)]
fid_centerline = open("tmp.centerline_coordinates.txt", "w")
for iz in range(0, nz, 1):
# compute inverse cumulative fitted transformation matrix
fid = open(file_mat_inv_cumul_fit[iz], "w")
fid.write("%i %i %i %f\n" % (1, 0, 0, x_centerline_fit_full[iz] - x_init))
fid.write("%i %i %i %f\n" % (0, 1, 0, y_centerline_fit_full[iz] - y_init))
fid.write("%i %i %i %i\n" % (0, 0, 1, 0))
fid.write("%i %i %i %i\n" % (0, 0, 0, 1))
fid.close()
# compute forward cumulative fitted transformation matrix
sct.run("convert_xfm -omat " + file_mat_cumul_fit[iz] + " -inverse " + file_mat_inv_cumul_fit[iz])
# write centerline coordinates in x, y, z format
fid_centerline.write(
"%f %f %f\n" % (x_centerline_fit_full[iz], y_centerline_fit_full[iz], z_centerline_full[iz])
)
fid_centerline.close()
# Prepare output data
# ====================================================================================================
# write centerline as text file
for iz in range(0, nz, 1):
# compute inverse cumulative fitted transformation matrix
fid = open(file_mat_inv_cumul_fit[iz], "w")
fid.write("%i %i %i %f\n" % (1, 0, 0, x_centerline_fit_full[iz] - x_init))
fid.write("%i %i %i %f\n" % (0, 1, 0, y_centerline_fit_full[iz] - y_init))
fid.write("%i %i %i %i\n" % (0, 0, 1, 0))
fid.write("%i %i %i %i\n" % (0, 0, 0, 1))
fid.close()
# write polynomial coefficients
savetxt("tmp.centerline_polycoeffs_x.txt", coeffsx)
savetxt("tmp.centerline_polycoeffs_y.txt", coeffsy)
# apply transformations to data
print "\nApply fitted transformation matrices..."
file_anat_split_fit = ["tmp.anat_orient_fit_z" + str(z).zfill(4) for z in range(0, nz, 1)]
file_mask_split_fit = ["tmp.mask_orient_fit_z" + str(z).zfill(4) for z in range(0, nz, 1)]
file_point_split_fit = ["tmp.point_orient_fit_z" + str(z).zfill(4) for z in range(0, nz, 1)]
for iz in range(0, nz, 1):
# forward cumulative transformation to data
sct.run(
fsloutput
+ "flirt -in "
+ file_anat_split[iz]
+ " -ref "
+ file_anat_split[iz]
+ " -applyxfm -init "
+ file_mat_cumul_fit[iz]
+ " -out "
+ file_anat_split_fit[iz]
)
# inverse cumulative transformation to mask
sct.run(
fsloutput
+ "flirt -in "
+ file_mask_split[z_init]
+ " -ref "
+ file_mask_split[z_init]
+ " -applyxfm -init "
+ file_mat_inv_cumul_fit[iz]
+ " -out "
+ file_mask_split_fit[iz]
)
# inverse cumulative transformation to point
sct.run(
fsloutput
+ "flirt -in "
+ file_point_split[z_init]
+ " -ref "
+ file_point_split[z_init]
+ " -applyxfm -init "
+ file_mat_inv_cumul_fit[iz]
+ " -out "
+ file_point_split_fit[iz]
+ " -interp nearestneighbour"
)
# Merge into 4D volume
print "\nMerge into 4D volume..."
# im_anat_list = [Image(fname) for fname in glob.glob('tmp.anat_orient_fit_z*.nii')]
fname_anat_list = glob.glob("tmp.anat_orient_fit_z*.nii")
im_anat_concat = concat_data(fname_anat_list, 2)
im_anat_concat.setFileName("tmp.anat_orient_fit.nii")
im_anat_concat.save()
# im_mask_list = [Image(fname) for fname in glob.glob('tmp.mask_orient_fit_z*.nii')]
fname_mask_list = glob.glob("tmp.mask_orient_fit_z*.nii")
im_mask_concat = concat_data(fname_mask_list, 2)
im_mask_concat.setFileName("tmp.mask_orient_fit.nii")
im_mask_concat.save()
# im_point_list = [Image(fname) for fname in glob.glob('tmp.point_orient_fit_z*.nii')]
fname_point_list = glob.glob("tmp.point_orient_fit_z*.nii")
im_point_concat = concat_data(fname_point_list, 2)
im_point_concat.setFileName("tmp.point_orient_fit.nii")
im_point_concat.save()
# Copy header geometry from input data
print "\nCopy header geometry from input data..."
im_anat = Image("tmp.anat_orient.nii")
im_anat_orient_fit = Image("tmp.anat_orient_fit.nii")
im_mask_orient_fit = Image("tmp.mask_orient_fit.nii")
im_point_orient_fit = Image("tmp.point_orient_fit.nii")
im_anat_orient_fit = copy_header(im_anat, im_anat_orient_fit)
im_mask_orient_fit = copy_header(im_anat, im_mask_orient_fit)
im_point_orient_fit = copy_header(im_anat, im_point_orient_fit)
for im in [im_anat_orient_fit, im_mask_orient_fit, im_point_orient_fit]:
im.save()
# Reorient outputs into the initial orientation of the input image
print "\nReorient the centerline into the initial orientation of the input image..."
set_orientation("tmp.point_orient_fit.nii", input_image_orientation, "tmp.point_orient_fit.nii")
set_orientation("tmp.mask_orient_fit.nii", input_image_orientation, "tmp.mask_orient_fit.nii")
# Generate output file (in current folder)
print "\nGenerate output file (in current folder)..."
os.chdir("..") # come back to parent folder
fname_output_centerline = sct.generate_output_file(
path_tmp + "/tmp.point_orient_fit.nii", file_anat + "_centerline" + ext_anat
)
# Delete temporary files
if remove_tmp_files == 1:
print "\nRemove temporary files..."
sct.run("rm -rf " + path_tmp, error_exit="warning")
# print number of warnings
print "\nNumber of warnings: " + str(
warning_count
) + " (if >10, you should probably reduce the gap and/or increase the kernel size"
# display elapsed time
elapsed_time = time() - start_time
print "\nFinished! \n\tGenerated file: " + fname_output_centerline + "\n\tElapsed time: " + str(
int(round(elapsed_time))
) + "s\n"
def moco(param):
# retrieve parameters
file_data = param.file_data
file_target = param.file_target
folder_mat = param.mat_moco # output folder of mat file
todo = param.todo
suffix = param.suffix
verbose = param.verbose
# other parameters
file_mask = 'mask.nii'
sct.printv('\nInput parameters:', param.verbose)
sct.printv(' Input file ............' + file_data, param.verbose)
sct.printv(' Reference file ........' + file_target, param.verbose)
sct.printv(' Polynomial degree .....' + param.poly, param.verbose)
sct.printv(' Smoothing kernel ......' + param.smooth, param.verbose)
sct.printv(' Gradient step .........' + param.gradStep, param.verbose)
sct.printv(' Metric ................' + param.metric, param.verbose)
sct.printv(' Sampling ..............' + param.sampling, param.verbose)
sct.printv(' Todo ..................' + todo, param.verbose)
sct.printv(' Mask .................' + param.fname_mask, param.verbose)
sct.printv(' Output mat folder .....' + folder_mat, param.verbose)
# create folder for mat files
sct.create_folder(folder_mat)
# Get size of data
sct.printv('\nData dimensions:', verbose)
im_data = Image(param.file_data)
nx, ny, nz, nt, px, py, pz, pt = im_data.dim
sct.printv(
(' ' + str(nx) + ' x ' + str(ny) + ' x ' + str(nz) + ' x ' + str(nt)),
verbose)
# copy file_target to a temporary file
sct.printv('\nCopy file_target to a temporary file...', verbose)
file_target = "target.nii.gz"
convert(param.file_target, file_target)
# If scan is sagittal, split src and target along Z (slice)
if param.is_sagittal:
dim_sag = 2 # TODO: find it
# z-split data (time series)
im_z_list = split_data(im_data, dim=dim_sag, squeeze_data=False)
file_data_splitZ = []
for im_z in im_z_list:
im_z.save()
file_data_splitZ.append(im_z.absolutepath)
# z-split target
im_targetz_list = split_data(Image(file_target),
dim=dim_sag,
squeeze_data=False)
file_target_splitZ = []
for im_targetz in im_targetz_list:
im_targetz.save()
file_target_splitZ.append(im_targetz.absolutepath)
# z-split mask (if exists)
if not param.fname_mask == '':
im_maskz_list = split_data(Image(file_mask),
dim=dim_sag,
squeeze_data=False)
file_mask_splitZ = []
for im_maskz in im_maskz_list:
im_maskz.save()
file_mask_splitZ.append(im_maskz.absolutepath)
# initialize file list for output matrices
file_mat = np.empty((nz, nt), dtype=object)
# axial orientation
else:
file_data_splitZ = [file_data] # TODO: make it absolute like above
file_target_splitZ = [file_target] # TODO: make it absolute like above
# initialize file list for output matrices
file_mat = np.empty((1, nt), dtype=object)
# deal with mask
if not param.fname_mask == '':
convert(param.fname_mask, file_mask, squeeze_data=False)
im_maskz_list = [Image(file_mask)
] # use a list with single element
# Loop across file list, where each file is either a 2D volume (if sagittal) or a 3D volume (otherwise)
# file_mat = tuple([[[] for i in range(nt)] for i in range(nz)])
file_data_splitZ_moco = []
sct.printv(
'\nRegister. Loop across Z (note: there is only one Z if orientation is axial'
)
for file in file_data_splitZ:
iz = file_data_splitZ.index(file)
# Split data along T dimension
# sct.printv('\nSplit data along T dimension.', verbose)
im_z = Image(file)
list_im_zt = split_data(im_z, dim=3)
file_data_splitZ_splitT = []
for im_zt in list_im_zt:
im_zt.save(verbose=0)
file_data_splitZ_splitT.append(im_zt.absolutepath)
# file_data_splitT = file_data + '_T'
# Motion correction: initialization
index = np.arange(nt)
file_data_splitT_num = []
file_data_splitZ_splitT_moco = []
failed_transfo = [0 for i in range(nt)]
# Motion correction: Loop across T
for indice_index in tqdm(range(nt),
unit='iter',
unit_scale=False,
desc="Z=" + str(iz) + "/" +
str(len(file_data_splitZ) - 1),
ascii=True,
ncols=80):
# create indices and display stuff
it = index[indice_index]
file_mat[iz][it] = os.path.join(
folder_mat,
"mat.Z") + str(iz).zfill(4) + 'T' + str(it).zfill(4)
file_data_splitZ_splitT_moco.append(
sct.add_suffix(file_data_splitZ_splitT[it], '_moco'))
# deal with masking
if not param.fname_mask == '':
input_mask = im_maskz_list[iz]
else:
input_mask = None
# run 3D registration
failed_transfo[it] = register(param,
file_data_splitZ_splitT[it],
file_target_splitZ[iz],
file_mat[iz][it],
file_data_splitZ_splitT_moco[it],
im_mask=input_mask)
# average registered volume with target image
# N.B. use weighted averaging: (target * nb_it + moco) / (nb_it + 1)
if param.iterAvg and indice_index < 10 and failed_transfo[
it] == 0 and not param.todo == 'apply':
im_targetz = Image(file_target_splitZ[iz])
data_targetz = im_targetz.data
data_mocoz = Image(file_data_splitZ_splitT_moco[it]).data
data_targetz = (data_targetz * (indice_index + 1) +
data_mocoz) / (indice_index + 2)
im_targetz.data = data_targetz
im_targetz.save(verbose=0)
# Replace failed transformation with the closest good one
fT = [i for i, j in enumerate(failed_transfo) if j == 1]
gT = [i for i, j in enumerate(failed_transfo) if j == 0]
for it in range(len(fT)):
abs_dist = [np.abs(gT[i] - fT[it]) for i in range(len(gT))]
if not abs_dist == []:
index_good = abs_dist.index(min(abs_dist))
sct.printv(
' transfo #' + str(fT[it]) + ' --> use transfo #' +
str(gT[index_good]), verbose)
# copy transformation
sct.copy(file_mat[iz][gT[index_good]] + 'Warp.nii.gz',
file_mat[iz][fT[it]] + 'Warp.nii.gz')
# apply transformation
sct_apply_transfo.main(args=[
'-i', file_data_splitZ_splitT[fT[it]], '-d', file_target,
'-w', file_mat[iz][fT[it]] + 'Warp.nii.gz', '-o',
file_data_splitZ_splitT_moco[fT[it]], '-x', param.interp
])
else:
# exit program if no transformation exists.
sct.printv(
'\nERROR in ' + os.path.basename(__file__) +
': No good transformation exist. Exit program.\n', verbose,
'error')
sys.exit(2)
# Merge data along T
file_data_splitZ_moco.append(sct.add_suffix(file, suffix))
if todo != 'estimate':
im_out = concat_data(file_data_splitZ_splitT_moco, 3)
im_out.save(file_data_splitZ_moco[iz])
# If sagittal, merge along Z
if param.is_sagittal:
im_out = concat_data(file_data_splitZ_moco, 2)
dirname, basename, ext = sct.extract_fname(file_data)
path_out = os.path.join(dirname, basename + suffix + ext)
im_out.save(path_out)
return file_mat
def dmri_moco(param):
file_data = 'dmri'
ext_data = '.nii'
file_b0 = 'b0'
file_dwi = 'dwi'
mat_final = 'mat_final/'
file_dwi_group = 'dwi_averaged_groups' # no extension
ext_mat = 'Warp.nii.gz' # warping field
# Get dimensions of data
sct.printv('\nGet dimensions of data...', param.verbose)
im_data = Image(file_data + ext_data)
nx, ny, nz, nt, px, py, pz, pt = im_data.dim
sct.printv(' ' + str(nx) + ' x ' + str(ny) + ' x ' + str(nz),
param.verbose)
# Identify b=0 and DWI images
sct.printv('\nIdentify b=0 and DWI images...', param.verbose)
index_b0, index_dwi, nb_b0, nb_dwi = identify_b0('bvecs.txt',
param.fname_bvals,
param.bval_min,
param.verbose)
# check if dmri and bvecs are the same size
if not nb_b0 + nb_dwi == nt:
sct.printv(
'\nERROR in ' + os.path.basename(__file__) + ': Size of data (' +
str(nt) + ') and size of bvecs (' + str(nb_b0 + nb_dwi) +
') are not the same. Check your bvecs file.\n', 1, 'error')
sys.exit(2)
# Prepare NIFTI (mean/groups...)
#===================================================================================================================
# Split into T dimension
sct.printv('\nSplit along T dimension...', param.verbose)
im_data_split_list = split_data(im_data, 3)
for im in im_data_split_list:
im.save()
# Merge b=0 images
sct.printv('\nMerge b=0...', param.verbose)
im_b0_list = []
for it in range(nb_b0):
im_b0_list.append(im_data_split_list[index_b0[it]])
im_b0_out = concat_data(im_b0_list, 3)
im_b0_out.setFileName(file_b0 + ext_data)
im_b0_out.save()
sct.printv((' File created: ' + file_b0), param.verbose)
# Average b=0 images
sct.printv('\nAverage b=0...', param.verbose)
file_b0_mean = file_b0 + '_mean'
sct.run(
'sct_maths -i ' + file_b0 + ext_data + ' -o ' + file_b0_mean +
ext_data + ' -mean t', param.verbose)
# Number of DWI groups
nb_groups = int(math.floor(nb_dwi / param.group_size))
# Generate groups indexes
group_indexes = []
for iGroup in range(nb_groups):
group_indexes.append(index_dwi[(iGroup *
param.group_size):((iGroup + 1) *
param.group_size)])
# add the remaining images to the last DWI group
nb_remaining = nb_dwi % param.group_size # number of remaining images
if nb_remaining > 0:
nb_groups += 1
group_indexes.append(index_dwi[len(index_dwi) -
nb_remaining:len(index_dwi)])
# DWI groups
file_dwi_mean = []
for iGroup in range(nb_groups):
sct.printv('\nDWI group: ' + str((iGroup + 1)) + '/' + str(nb_groups),
param.verbose)
# get index
index_dwi_i = group_indexes[iGroup]
nb_dwi_i = len(index_dwi_i)
# Merge DW Images
sct.printv('Merge DW images...', param.verbose)
file_dwi_merge_i = file_dwi + '_' + str(iGroup)
im_dwi_list = []
for it in range(nb_dwi_i):
im_dwi_list.append(im_data_split_list[index_dwi_i[it]])
im_dwi_out = concat_data(im_dwi_list, 3)
im_dwi_out.setFileName(file_dwi_merge_i + ext_data)
im_dwi_out.save()
# Average DW Images
sct.printv('Average DW images...', param.verbose)
file_dwi_mean.append(file_dwi + '_mean_' + str(iGroup))
sct.run(
'sct_maths -i ' + file_dwi_merge_i + ext_data + ' -o ' +
file_dwi_mean[iGroup] + ext_data + ' -mean t', param.verbose)
# Merge DWI groups means
sct.printv('\nMerging DW files...', param.verbose)
# file_dwi_groups_means_merge = 'dwi_averaged_groups'
im_dw_list = []
for iGroup in range(nb_groups):
im_dw_list.append(file_dwi_mean[iGroup] + ext_data)
im_dw_out = concat_data(im_dw_list, 3)
im_dw_out.setFileName(file_dwi_group + ext_data)
im_dw_out.save()
# Average DW Images
# TODO: USEFULL ???
sct.printv('\nAveraging all DW images...', param.verbose)
sct.run(
'sct_maths -i ' + file_dwi_group + ext_data + ' -o ' + file_dwi_group +
'_mean' + ext_data + ' -mean t', param.verbose)
# segment dwi images using otsu algorithm
if param.otsu:
sct.printv('\nSegment group DWI using OTSU algorithm...',
param.verbose)
# import module
otsu = importlib.import_module('sct_otsu')
# get class from module
param_otsu = otsu.param() #getattr(otsu, param)
param_otsu.fname_data = file_dwi_group + ext_data
param_otsu.threshold = param.otsu
param_otsu.file_suffix = '_seg'
# run otsu
otsu.otsu(param_otsu)
file_dwi_group = file_dwi_group + '_seg'
# START MOCO
#===================================================================================================================
# Estimate moco on b0 groups
sct.printv(
'\n-------------------------------------------------------------------------------',
param.verbose)
sct.printv(' Estimating motion on b=0 images...', param.verbose)
sct.printv(
'-------------------------------------------------------------------------------',
param.verbose)
param_moco = param
param_moco.file_data = 'b0'
# identify target image
if index_dwi[0] != 0:
# If first DWI is not the first volume (most common), then there is a least one b=0 image before. In that case
# select it as the target image for registration of all b=0
param_moco.file_target = file_data + '_T' + str(
index_b0[index_dwi[0] - 1]).zfill(4)
else:
# If first DWI is the first volume, then the target b=0 is the first b=0 from the index_b0.
param_moco.file_target = file_data + '_T' + str(index_b0[0]).zfill(4)
param_moco.path_out = ''
param_moco.todo = 'estimate'
param_moco.mat_moco = 'mat_b0groups'
moco.moco(param_moco)
# Estimate moco on dwi groups
sct.printv(
'\n-------------------------------------------------------------------------------',
param.verbose)
sct.printv(' Estimating motion on DW images...', param.verbose)
sct.printv(
'-------------------------------------------------------------------------------',
param.verbose)
param_moco.file_data = file_dwi_group
param_moco.file_target = file_dwi_mean[
0] # target is the first DW image (closest to the first b=0)
param_moco.path_out = ''
param_moco.todo = 'estimate_and_apply'
param_moco.mat_moco = 'mat_dwigroups'
moco.moco(param_moco)
# create final mat folder
sct.create_folder(mat_final)
# Copy b=0 registration matrices
sct.printv('\nCopy b=0 registration matrices...', param.verbose)
for it in range(nb_b0):
sct.run(
'cp ' + 'mat_b0groups/' + 'mat.T' + str(it) + ext_mat + ' ' +
mat_final + 'mat.T' + str(index_b0[it]) + ext_mat, param.verbose)
# Copy DWI registration matrices
sct.printv('\nCopy DWI registration matrices...', param.verbose)
for iGroup in range(nb_groups):
for dwi in range(len(group_indexes[iGroup])):
sct.run(
'cp ' + 'mat_dwigroups/' + 'mat.T' + str(iGroup) + ext_mat +
' ' + mat_final + 'mat.T' + str(group_indexes[iGroup][dwi]) +
ext_mat, param.verbose)
# Spline Regularization along T
if param.spline_fitting:
moco.spline(mat_final, nt, nz, param.verbose, np.array(index_b0),
param.plot_graph)
# combine Eddy Matrices
if param.run_eddy:
param.mat_2_combine = 'mat_eddy'
param.mat_final = mat_final
moco.combine_matrix(param)
# Apply moco on all dmri data
sct.printv(
'\n-------------------------------------------------------------------------------',
param.verbose)
sct.printv(' Apply moco', param.verbose)
sct.printv(
'-------------------------------------------------------------------------------',
param.verbose)
param_moco.file_data = file_data
param_moco.file_target = file_dwi + '_mean_' + str(
0) # reference for reslicing into proper coordinate system
param_moco.path_out = ''
param_moco.mat_moco = mat_final
param_moco.todo = 'apply'
moco.moco(param_moco)
# copy geometric information from header
# NB: this is required because WarpImageMultiTransform in 2D mode wrongly sets pixdim(3) to "1".
im_dmri = Image(file_data + ext_data)
im_dmri_moco = Image(file_data + param.suffix + ext_data)
im_dmri_moco = copy_header(im_dmri, im_dmri_moco)
im_dmri_moco.save()
# generate b0_moco_mean and dwi_moco_mean
cmd = 'sct_dmri_separate_b0_and_dwi -i ' + file_data + param.suffix + ext_data + ' -bvec bvecs.txt -a 1'
if not param.fname_bvals == '':
cmd = cmd + ' -m ' + param.fname_bvals
sct.run(cmd, param.verbose)
def find_centerline(algo, image_fname, contrast_type, brain_bool,
folder_output, remove_temp_files, centerline_fname):
"""
Assumes RPI orientation
:param algo:
:param image_fname:
:param contrast_type:
:param brain_bool:
:param folder_output:
:param remove_temp_files:
:param centerline_fname:
:return:
"""
# TODO: remove unnecessary i/o
if Image(image_fname).dim[2] == 1: # isct_spine_detect requires nz > 1
im_concat = concat_data([image_fname, image_fname], dim=2)
im_concat.save(sct.add_suffix(image_fname, '_concat'))
image_fname = sct.add_suffix(image_fname, '_concat')
bool_2d = True
else:
bool_2d = False
# TODO: maybe change 'svm' for 'optic', because this is how we call it in sct_get_centerline
if algo == 'svm':
# run optic on a heatmap computed by a trained SVM+HoG algorithm
# optic_models_fname = os.path.join(path_sct, 'data', 'optic_models', '{}_model'.format(contrast_type))
# # TODO: replace with get_centerline(method=optic)
img_ctl, arr_ctl, _ = get_centerline(Image(image_fname),
algo_fitting='optic',
contrast=contrast_type)
centerline_filename = sct.add_suffix(image_fname, "_ctr")
img_ctl.save(centerline_filename)
elif algo == 'cnn':
# CNN parameters
dct_patch_ctr = {
't2': {
'size': (80, 80),
'mean': 51.1417,
'std': 57.4408
},
't2s': {
'size': (80, 80),
'mean': 68.8591,
'std': 71.4659
},
't1': {
'size': (80, 80),
'mean': 55.7359,
'std': 64.3149
},
'dwi': {
'size': (80, 80),
'mean': 55.744,
'std': 45.003
}
}
dct_params_ctr = {
't2': {
'features': 16,
'dilation_layers': 2
},
't2s': {
'features': 8,
'dilation_layers': 3
},
't1': {
'features': 24,
'dilation_layers': 3
},
'dwi': {
'features': 8,
'dilation_layers': 2
}
}
# load model
ctr_model_fname = os.path.join(sct.__sct_dir__, 'data',
'deepseg_sc_models',
'{}_ctr.h5'.format(contrast_type))
ctr_model = nn_architecture_ctr(
height=dct_patch_ctr[contrast_type]['size'][0],
width=dct_patch_ctr[contrast_type]['size'][1],
channels=1,
classes=1,
features=dct_params_ctr[contrast_type]['features'],
depth=2,
temperature=1.0,
padding='same',
batchnorm=True,
dropout=0.0,
dilation_layers=dct_params_ctr[contrast_type]['dilation_layers'])
ctr_model.load_weights(ctr_model_fname)
logger.info("Resample the image to 0.5x0.5 mm in-plane resolution...")
fname_res = sct.add_suffix(image_fname, '_resampled')
input_resolution = Image(image_fname).dim[4:7]
new_resolution = 'x'.join(['0.5', '0.5', str(input_resolution[2])])
resampling.resample_file(image_fname,
fname_res,
new_resolution,
'mm',
'linear',
verbose=0)
# compute the heatmap
fname_heatmap = sct.add_suffix(image_fname, "_heatmap")
img_filename = ''.join(sct.extract_fname(fname_heatmap)[:2])
fname_heatmap_nii = img_filename + '.nii'
z_max = heatmap(filename_in=fname_res,
filename_out=fname_heatmap_nii,
model=ctr_model,
patch_shape=dct_patch_ctr[contrast_type]['size'],
mean_train=dct_patch_ctr[contrast_type]['mean'],
std_train=dct_patch_ctr[contrast_type]['std'],
brain_bool=brain_bool)
# run optic on the heatmap
centerline_filename = sct.add_suffix(fname_heatmap, "_ctr")
heatmap2optic(fname_heatmap=fname_heatmap_nii,
lambda_value=7 if contrast_type == 't2s' else 1,
fname_out=centerline_filename,
z_max=z_max if brain_bool else None)
elif algo == 'viewer':
im_labels = _call_viewer_centerline(Image(image_fname))
im_centerline, arr_centerline, _ = get_centerline(im_labels)
centerline_filename = sct.add_suffix(image_fname, "_ctr")
labels_filename = sct.add_suffix(image_fname, "_labels-centerline")
im_centerline.save(centerline_filename)
im_labels.save(labels_filename)
elif algo == 'file':
centerline_filename = sct.add_suffix(image_fname, "_ctr")
# Re-orient the manual centerline
Image(centerline_fname).change_orientation('RPI').save(
centerline_filename)
else:
logger.error(
'The parameter "-centerline" is incorrect. Please try again.')
sys.exit(1)
if algo != 'cnn':
logger.info("Resample the image to 0.5x0.5 mm in-plane resolution...")
fname_res = sct.add_suffix(image_fname, '_resampled')
input_resolution = Image(image_fname).dim[4:7]
new_resolution = 'x'.join(['0.5', '0.5', str(input_resolution[2])])
resampling.resample_file(image_fname,
fname_res,
new_resolution,
'mm',
'linear',
verbose=0)
resampling.resample_file(centerline_filename,
centerline_filename,
new_resolution,
'mm',
'linear',
verbose=0)
if bool_2d:
im_split_lst = split_data(Image(centerline_filename), dim=2)
im_split_lst[0].save(centerline_filename)
return fname_res, centerline_filename
def apply(self):
# Initialization
fname_src = self.input_filename # source image (moving)
list_warp = self.list_warp # list of warping fields
fname_out = self.output_filename # output
fname_dest = self.fname_dest # destination image (fix)
verbose = self.verbose
remove_temp_files = self.remove_temp_files
crop_reference = self.crop # if = 1, put 0 everywhere around warping field, if = 2, real crop
islabel = False
if self.interp == 'label':
islabel = True
self.interp = 'nn'
interp = sct.get_interpolation('isct_antsApplyTransforms', self.interp)
# Parse list of warping fields
sct.printv('\nParse list of warping fields...', verbose)
use_inverse = []
fname_warp_list_invert = []
# list_warp = list_warp.replace(' ', '') # remove spaces
# list_warp = list_warp.split(",") # parse with comma
for idx_warp, path_warp in enumerate(self.list_warp):
# Check if this transformation should be inverted
if path_warp in self.list_warpinv:
use_inverse.append('-i')
# list_warp[idx_warp] = path_warp[1:] # remove '-'
fname_warp_list_invert += [[use_inverse[idx_warp], list_warp[idx_warp]]]
else:
use_inverse.append('')
fname_warp_list_invert += [[path_warp]]
path_warp = list_warp[idx_warp]
if path_warp.endswith((".nii", ".nii.gz")) \
and Image(list_warp[idx_warp]).header.get_intent()[0] != 'vector':
raise ValueError("Displacement field in {} is invalid: should be encoded" \
" in a 5D file with vector intent code" \
" (see https://nifti.nimh.nih.gov/pub/dist/src/niftilib/nifti1.h" \
.format(path_warp))
# need to check if last warping field is an affine transfo
isLastAffine = False
path_fname, file_fname, ext_fname = sct.extract_fname(fname_warp_list_invert[-1][-1])
if ext_fname in ['.txt', '.mat']:
isLastAffine = True
## check if destination file is 3d
# sct.check_dim(fname_dest, dim_lst=[3]) # PR 2598: we decided to skip this line.
# N.B. Here we take the inverse of the warp list, because sct_WarpImageMultiTransform concatenates in the reverse order
fname_warp_list_invert.reverse()
fname_warp_list_invert = functools.reduce(lambda x, y: x + y, fname_warp_list_invert)
# Extract path, file and extension
path_src, file_src, ext_src = sct.extract_fname(fname_src)
path_dest, file_dest, ext_dest = sct.extract_fname(fname_dest)
# Get output folder and file name
if fname_out == '':
path_out = '' # output in user's current directory
file_out = file_src + '_reg'
ext_out = ext_src
fname_out = os.path.join(path_out, file_out + ext_out)
# Get dimensions of data
sct.printv('\nGet dimensions of data...', verbose)
img_src = Image(fname_src)
nx, ny, nz, nt, px, py, pz, pt = img_src.dim
# nx, ny, nz, nt, px, py, pz, pt = sct.get_dimension(fname_src)
sct.printv(' ' + str(nx) + ' x ' + str(ny) + ' x ' + str(nz) + ' x ' + str(nt), verbose)
# if 3d
if nt == 1:
# Apply transformation
sct.printv('\nApply transformation...', verbose)
if nz in [0, 1]:
dim = '2'
else:
dim = '3'
# if labels, dilate before resampling
if islabel:
sct.printv("\nDilate labels before warping...")
path_tmp = sct.tmp_create(basename="apply_transfo", verbose=verbose)
fname_dilated_labels = os.path.join(path_tmp, "dilated_data.nii")
# dilate points
dilate(Image(fname_src), 2, 'ball').save(fname_dilated_labels)
fname_src = fname_dilated_labels
sct.printv("\nApply transformation and resample to destination space...", verbose)
run_proc(['isct_antsApplyTransforms',
'-d', dim,
'-i', fname_src,
'-o', fname_out,
'-t'
] + fname_warp_list_invert + ['-r', fname_dest] + interp, verbose=verbose, is_sct_binary=True)
# if 4d, loop across the T dimension
else:
if islabel:
raise NotImplementedError
dim = '4'
path_tmp = sct.tmp_create(basename="apply_transfo", verbose=verbose)
# convert to nifti into temp folder
sct.printv('\nCopying input data to tmp folder and convert to nii...', verbose)
img_src.save(os.path.join(path_tmp, "data.nii"))
sct.copy(fname_dest, os.path.join(path_tmp, file_dest + ext_dest))
fname_warp_list_tmp = []
for fname_warp in list_warp:
path_warp, file_warp, ext_warp = sct.extract_fname(fname_warp)
sct.copy(fname_warp, os.path.join(path_tmp, file_warp + ext_warp))
fname_warp_list_tmp.append(file_warp + ext_warp)
fname_warp_list_invert_tmp = fname_warp_list_tmp[::-1]
curdir = os.getcwd()
os.chdir(path_tmp)
# split along T dimension
sct.printv('\nSplit along T dimension...', verbose)
im_dat = Image('data.nii')
im_header = im_dat.hdr
data_split_list = sct_image.split_data(im_dat, 3)
for im in data_split_list:
im.save()
# apply transfo
sct.printv('\nApply transformation to each 3D volume...', verbose)
for it in range(nt):
file_data_split = 'data_T' + str(it).zfill(4) + '.nii'
file_data_split_reg = 'data_reg_T' + str(it).zfill(4) + '.nii'
status, output = run_proc(['isct_antsApplyTransforms',
'-d', '3',
'-i', file_data_split,
'-o', file_data_split_reg,
'-t',
] + fname_warp_list_invert_tmp + [
'-r', file_dest + ext_dest,
] + interp, verbose, is_sct_binary=True)
# Merge files back
sct.printv('\nMerge file back...', verbose)
import glob
path_out, name_out, ext_out = sct.extract_fname(fname_out)
# im_list = [Image(file_name) for file_name in glob.glob('data_reg_T*.nii')]
# concat_data use to take a list of image in input, now takes a list of file names to open the files one by one (see issue #715)
fname_list = glob.glob('data_reg_T*.nii')
fname_list.sort()
im_out = sct_image.concat_data(fname_list, 3, im_header['pixdim'])
im_out.save(name_out + ext_out)
os.chdir(curdir)
sct.generate_output_file(os.path.join(path_tmp, name_out + ext_out), fname_out)
# Delete temporary folder if specified
if remove_temp_files:
sct.printv('\nRemove temporary files...', verbose)
sct.rmtree(path_tmp, verbose=verbose)
# Copy affine matrix from destination space to make sure qform/sform are the same
sct.printv("Copy affine matrix from destination space to make sure qform/sform are the same.", verbose)
im_src_reg = Image(fname_out)
im_src_reg.copy_qform_from_ref(Image(fname_dest))
im_src_reg.save(verbose=0) # set verbose=0 to avoid warning message about rewriting file
if islabel:
sct.printv("\nTake the center of mass of each registered dilated labels...")
labeled_img = cubic_to_point(im_src_reg)
labeled_img.save(path=fname_out)
if remove_temp_files:
sct.printv('\nRemove temporary files...', verbose)
sct.rmtree(path_tmp, verbose=verbose)
# Crop the resulting image using dimensions from the warping field
warping_field = fname_warp_list_invert[-1]
# If the last transformation is not an affine transfo, we need to compute the matrix space of the concatenated
# warping field
if not isLastAffine and crop_reference in [1, 2]:
sct.printv('Last transformation is not affine.')
if crop_reference in [1, 2]:
# Extract only the first ndim of the warping field
img_warp = Image(warping_field)
if dim == '2':
img_warp_ndim = Image(img_src.data[:, :], hdr=img_warp.hdr)
elif dim in ['3', '4']:
img_warp_ndim = Image(img_src.data[:, :, :], hdr=img_warp.hdr)
# Set zero to everything outside the warping field
cropper = ImageCropper(Image(fname_out))
cropper.get_bbox_from_ref(img_warp_ndim)
if crop_reference == 1:
sct.printv('Cropping strategy is: keep same matrix size, put 0 everywhere around warping field')
img_out = cropper.crop(background=0)
elif crop_reference == 2:
sct.printv('Cropping strategy is: crop around warping field (the size of warping field will '
'change)')
img_out = cropper.crop()
img_out.save(fname_out)
sct.display_viewer_syntax([fname_dest, fname_out], verbose=verbose)
def dmri_moco(param):
file_data = 'dmri.nii'
file_data_dirname, file_data_basename, file_data_ext = sct.extract_fname(
file_data)
file_b0 = 'b0.nii'
file_dwi = 'dwi.nii'
ext_data = '.nii.gz' # workaround "too many open files" by slurping the data
mat_final = 'mat_final/'
file_dwi_group = 'dwi_averaged_groups.nii'
ext_mat = 'Warp.nii.gz' # warping field
# Get dimensions of data
sct.printv('\nGet dimensions of data...', param.verbose)
im_data = Image(file_data)
nx, ny, nz, nt, px, py, pz, pt = im_data.dim
sct.printv(' ' + str(nx) + ' x ' + str(ny) + ' x ' + str(nz),
param.verbose)
# Identify b=0 and DWI images
index_b0, index_dwi, nb_b0, nb_dwi = sct_dmri_separate_b0_and_dwi.identify_b0(
'bvecs.txt', param.fname_bvals, param.bval_min, param.verbose)
# check if dmri and bvecs are the same size
if not nb_b0 + nb_dwi == nt:
sct.printv(
'\nERROR in ' + os.path.basename(__file__) + ': Size of data (' +
str(nt) + ') and size of bvecs (' + str(nb_b0 + nb_dwi) +
') are not the same. Check your bvecs file.\n', 1, 'error')
sys.exit(2)
# Prepare NIFTI (mean/groups...)
#===================================================================================================================
# Split into T dimension
sct.printv('\nSplit along T dimension...', param.verbose)
im_data_split_list = split_data(im_data, 3)
for im in im_data_split_list:
x_dirname, x_basename, x_ext = sct.extract_fname(im.absolutepath)
im.absolutepath = os.path.join(x_dirname, x_basename + ".nii.gz")
im.save()
# Merge b=0 images
sct.printv('\nMerge b=0...', param.verbose)
im_b0_list = []
for it in range(nb_b0):
im_b0_list.append(im_data_split_list[index_b0[it]])
im_b0_out = concat_data(im_b0_list, 3).save(file_b0)
sct.printv((' File created: ' + file_b0), param.verbose)
# Average b=0 images
sct.printv('\nAverage b=0...', param.verbose)
file_b0_mean = sct.add_suffix(file_b0, '_mean')
sct.run(['sct_maths', '-i', file_b0, '-o', file_b0_mean, '-mean', 't'],
param.verbose)
# Number of DWI groups
nb_groups = int(math.floor(nb_dwi / param.group_size))
# Generate groups indexes
group_indexes = []
for iGroup in range(nb_groups):
group_indexes.append(index_dwi[(iGroup *
param.group_size):((iGroup + 1) *
param.group_size)])
# add the remaining images to the last DWI group
nb_remaining = nb_dwi % param.group_size # number of remaining images
if nb_remaining > 0:
nb_groups += 1
group_indexes.append(index_dwi[len(index_dwi) -
nb_remaining:len(index_dwi)])
file_dwi_dirname, file_dwi_basename, file_dwi_ext = sct.extract_fname(
file_dwi)
# DWI groups
file_dwi_mean = []
for iGroup in tqdm(range(nb_groups),
unit='iter',
unit_scale=False,
desc="Merge within groups",
ascii=True,
ncols=80):
# get index
index_dwi_i = group_indexes[iGroup]
nb_dwi_i = len(index_dwi_i)
# Merge DW Images
file_dwi_merge_i = os.path.join(
file_dwi_dirname, file_dwi_basename + '_' + str(iGroup) + ext_data)
im_dwi_list = []
for it in range(nb_dwi_i):
im_dwi_list.append(im_data_split_list[index_dwi_i[it]])
im_dwi_out = concat_data(im_dwi_list, 3).save(file_dwi_merge_i)
# Average DW Images
file_dwi_mean_i = os.path.join(
file_dwi_dirname,
file_dwi_basename + '_mean_' + str(iGroup) + ext_data)
file_dwi_mean.append(file_dwi_mean_i)
sct.run([
"sct_maths", "-i", file_dwi_merge_i, "-o", file_dwi_mean[iGroup],
"-mean", "t"
], 0)
# Merge DWI groups means
sct.printv('\nMerging DW files...', param.verbose)
# file_dwi_groups_means_merge = 'dwi_averaged_groups'
im_dw_list = []
for iGroup in range(nb_groups):
im_dw_list.append(file_dwi_mean[iGroup])
im_dw_out = concat_data(im_dw_list, 3).save(file_dwi_group)
# Average DW Images
# TODO: USEFULL ???
sct.printv('\nAveraging all DW images...', param.verbose)
sct.run([
"sct_maths", "-i", file_dwi_group, "-o",
file_dwi_group + '_mean' + ext_data, "-mean", "t"
], param.verbose)
# segment dwi images using otsu algorithm
if param.otsu:
sct.printv('\nSegment group DWI using OTSU algorithm...',
param.verbose)
# import module
otsu = importlib.import_module('sct_otsu')
# get class from module
param_otsu = otsu.param() #getattr(otsu, param)
param_otsu.fname_data = file_dwi_group
param_otsu.threshold = param.otsu
param_otsu.file_suffix = '_seg'
# run otsu
otsu.otsu(param_otsu)
file_dwi_group = file_dwi_group + '_seg.nii'
# START MOCO
#===================================================================================================================
# Estimate moco on b0 groups
sct.printv(
'\n-------------------------------------------------------------------------------',
param.verbose)
sct.printv(' Estimating motion on b=0 images...', param.verbose)
sct.printv(
'-------------------------------------------------------------------------------',
param.verbose)
param_moco = param
param_moco.file_data = 'b0.nii'
# identify target image
if index_dwi[0] != 0:
# If first DWI is not the first volume (most common), then there is a least one b=0 image before. In that case
# select it as the target image for registration of all b=0
param_moco.file_target = os.path.join(
file_data_dirname, file_data_basename + '_T' +
str(index_b0[index_dwi[0] - 1]).zfill(4) + ext_data)
else:
# If first DWI is the first volume, then the target b=0 is the first b=0 from the index_b0.
param_moco.file_target = os.path.join(
file_data_dirname,
file_data_basename + '_T' + str(index_b0[0]).zfill(4) + ext_data)
param_moco.path_out = ''
param_moco.todo = 'estimate'
param_moco.mat_moco = 'mat_b0groups'
file_mat_b0 = moco.moco(param_moco)
# Estimate moco on dwi groups
sct.printv(
'\n-------------------------------------------------------------------------------',
param.verbose)
sct.printv(' Estimating motion on DW images...', param.verbose)
sct.printv(
'-------------------------------------------------------------------------------',
param.verbose)
param_moco.file_data = file_dwi_group
param_moco.file_target = file_dwi_mean[
0] # target is the first DW image (closest to the first b=0)
param_moco.path_out = ''
param_moco.todo = 'estimate_and_apply'
param_moco.mat_moco = 'mat_dwigroups'
file_mat_dwi = moco.moco(param_moco)
# create final mat folder
sct.create_folder(mat_final)
# Copy b=0 registration matrices
# TODO: use file_mat_b0 and file_mat_dwi instead of the hardcoding below
sct.printv('\nCopy b=0 registration matrices...', param.verbose)
for it in range(nb_b0):
sct.copy(
'mat_b0groups/' + 'mat.Z0000T' + str(it).zfill(4) + ext_mat,
mat_final + 'mat.Z0000T' + str(index_b0[it]).zfill(4) + ext_mat)
# Copy DWI registration matrices
sct.printv('\nCopy DWI registration matrices...', param.verbose)
for iGroup in range(nb_groups):
for dwi in range(
len(group_indexes[iGroup])
): # we cannot use enumerate because group_indexes has 2 dim.
sct.copy(
'mat_dwigroups/' + 'mat.Z0000T' + str(iGroup).zfill(4) +
ext_mat, mat_final + 'mat.Z0000T' +
str(group_indexes[iGroup][dwi]).zfill(4) + ext_mat)
# Spline Regularization along T
if param.spline_fitting:
moco.spline(mat_final, nt, nz, param.verbose, np.array(index_b0),
param.plot_graph)
# combine Eddy Matrices
if param.run_eddy:
param.mat_2_combine = 'mat_eddy'
param.mat_final = mat_final
moco.combine_matrix(param)
# Apply moco on all dmri data
sct.printv(
'\n-------------------------------------------------------------------------------',
param.verbose)
sct.printv(' Apply moco', param.verbose)
sct.printv(
'-------------------------------------------------------------------------------',
param.verbose)
param_moco.file_data = file_data
param_moco.file_target = os.path.join(
file_dwi_dirname, file_dwi_basename + '_mean_' + str(0) +
ext_data) # reference for reslicing into proper coordinate system
param_moco.path_out = ''
param_moco.mat_moco = mat_final
param_moco.todo = 'apply'
moco.moco(param_moco)
# copy geometric information from header
# NB: this is required because WarpImageMultiTransform in 2D mode wrongly sets pixdim(3) to "1".
im_dmri = Image(file_data)
fname_data_moco = os.path.join(file_data_dirname,
file_data_basename + param.suffix + '.nii')
im_dmri_moco = Image(fname_data_moco)
im_dmri_moco.header = im_dmri.header
im_dmri_moco.save()
return os.path.abspath(fname_data_moco)
def main(args = None):
orientation = ''
change_header = ''
fname_out = ''
if not args:
args = sys.argv[1:]
# Building the command, do sanity checks
parser = get_parser()
arguments = parser.parse(sys.argv[1:])
fname_in = arguments['-i']
if '-o' in arguments:
fname_out = arguments['-o']
if '-s' in arguments:
orientation = arguments['-s']
if '-a' in arguments:
change_header = arguments['-a']
remove_tmp_files = int(arguments['-r'])
verbose = int(arguments['-v'])
inversion = False # change orientation
# create temporary folder
sct.printv('\nCreate temporary folder...', verbose)
path_tmp = 'tmp.'+time.strftime("%y%m%d%H%M%S/")
status, output = sct.run('mkdir '+path_tmp, verbose)
# copy file in temp folder
sct.printv('\nCopy files to tmp folder...', verbose)
convert(fname_in, path_tmp+'data.nii', verbose=0)
# go to temp folder
os.chdir(path_tmp)
# Get dimensions of data
sct.printv('\nGet dimensions of data...', verbose)
nx, ny, nz, nt, px, py, pz, pt = Image('data.nii').dim
sct.printv(str(nx) + ' x ' + str(ny) + ' x ' + str(nz)+ ' x ' + str(nt), verbose)
# if data are 3d, directly set or get orientation
if nt == 1:
if orientation != '':
# set orientation
sct.printv('\nChange orientation...', verbose)
if change_header == '':
set_orientation('data.nii', orientation, 'data_orient.nii')
else:
set_orientation('data.nii', change_header, 'data_orient.nii', True)
else:
# get orientation
sct.printv('\nGet orientation...', verbose)
print get_orientation('data.nii')
else:
# split along T dimension
sct.printv('\nSplit along T dimension...', verbose)
im = Image('data.nii')
im_split_list = split_data(im, 3)
for im_s in im_split_list:
im_s.save()
if orientation != '':
# set orientation
sct.printv('\nChange orientation...', verbose)
for it in range(nt):
file_data_split = 'data_T'+str(it).zfill(4)+'.nii'
file_data_split_orient = 'data_orient_T'+str(it).zfill(4)+'.nii'
set_orientation(file_data_split, orientation, file_data_split_orient)
# Merge files back
sct.printv('\nMerge file back...', verbose)
from glob import glob
im_data_list = [Image(fname) for fname in glob('data_orient_T*.nii')]
im_concat = concat_data(im_data_list, 3)
im_concat.setFileName('data_orient.nii')
im_concat.save()
else:
sct.printv('\nGet orientation...', verbose)
print get_orientation('data_T0000.nii')
# come back to parent folder
os.chdir('..')
# Generate output files
if orientation != '':
# Build fname_out
if fname_out == '':
path_data, file_data, ext_data = sct.extract_fname(fname_in)
fname_out = path_data+file_data+'_'+orientation+ext_data
sct.printv('\nGenerate output files...', verbose)
sct.generate_output_file(path_tmp+'data_orient.nii', fname_out)
# Remove temporary files
if remove_tmp_files == 1:
sct.printv('\nRemove temporary files...', verbose)
sct.run('rm -rf '+path_tmp, verbose)
def main(args=None):
# initialize parameters
param = Param()
# call main function
parser = get_parser()
if args:
arguments = parser.parse_args(args)
else:
arguments = parser.parse_args(args=None if sys.argv[1:] else ['--help'])
fname_data = arguments.i
fname_bvecs = arguments.bvec
average = arguments.a
verbose = int(arguments.v)
init_sct(log_level=verbose, update=True) # Update log level
remove_temp_files = arguments.r
path_out = arguments.ofolder
fname_bvals = arguments.bval
if arguments.bvalmin:
param.bval_min = arguments.bvalmin
# Initialization
start_time = time.time()
# printv(arguments)
printv('\nInput parameters:', verbose)
printv(' input file ............' + fname_data, verbose)
printv(' bvecs file ............' + fname_bvecs, verbose)
printv(' bvals file ............' + fname_bvals, verbose)
printv(' average ...............' + str(average), verbose)
# Get full path
fname_data = os.path.abspath(fname_data)
fname_bvecs = os.path.abspath(fname_bvecs)
if fname_bvals:
fname_bvals = os.path.abspath(fname_bvals)
# Extract path, file and extension
path_data, file_data, ext_data = extract_fname(fname_data)
# create temporary folder
path_tmp = tmp_create(basename="dmri_separate")
# copy files into tmp folder and convert to nifti
printv('\nCopy files into temporary folder...', verbose)
ext = '.nii'
dmri_name = 'dmri'
b0_name = file_data + '_b0'
b0_mean_name = b0_name + '_mean'
dwi_name = file_data + '_dwi'
dwi_mean_name = dwi_name + '_mean'
if not convert(fname_data, os.path.join(path_tmp, dmri_name + ext)):
printv('ERROR in convert.', 1, 'error')
copy(fname_bvecs, os.path.join(path_tmp, "bvecs"), verbose=verbose)
# go to tmp folder
curdir = os.getcwd()
os.chdir(path_tmp)
# Get size of data
im_dmri = Image(dmri_name + ext)
printv('\nGet dimensions data...', verbose)
nx, ny, nz, nt, px, py, pz, pt = im_dmri.dim
printv('.. ' + str(nx) + ' x ' + str(ny) + ' x ' + str(nz) + ' x ' + str(nt), verbose)
# Identify b=0 and DWI images
printv(fname_bvals)
index_b0, index_dwi, nb_b0, nb_dwi = identify_b0(fname_bvecs, fname_bvals, param.bval_min, verbose)
# Split into T dimension
printv('\nSplit along T dimension...', verbose)
im_dmri_split_list = split_data(im_dmri, 3)
for im_d in im_dmri_split_list:
im_d.save()
# Merge b=0 images
printv('\nMerge b=0...', verbose)
from sct_image import concat_data
l = []
for it in range(nb_b0):
l.append(dmri_name + '_T' + str(index_b0[it]).zfill(4) + ext)
im_out = concat_data(l, 3).save(b0_name + ext)
# Average b=0 images
if average:
printv('\nAverage b=0...', verbose)
run_proc(['sct_maths', '-i', b0_name + ext, '-o', b0_mean_name + ext, '-mean', 't'], verbose)
# Merge DWI
l = []
for it in range(nb_dwi):
l.append(dmri_name + '_T' + str(index_dwi[it]).zfill(4) + ext)
im_out = concat_data(l, 3).save(dwi_name + ext)
# Average DWI images
if average:
printv('\nAverage DWI...', verbose)
run_proc(['sct_maths', '-i', dwi_name + ext, '-o', dwi_mean_name + ext, '-mean', 't'], verbose)
# come back
os.chdir(curdir)
# Generate output files
fname_b0 = os.path.abspath(os.path.join(path_out, b0_name + ext_data))
fname_dwi = os.path.abspath(os.path.join(path_out, dwi_name + ext_data))
fname_b0_mean = os.path.abspath(os.path.join(path_out, b0_mean_name + ext_data))
fname_dwi_mean = os.path.abspath(os.path.join(path_out, dwi_mean_name + ext_data))
printv('\nGenerate output files...', verbose)
generate_output_file(os.path.join(path_tmp, b0_name + ext), fname_b0, verbose=verbose)
generate_output_file(os.path.join(path_tmp, dwi_name + ext), fname_dwi, verbose=verbose)
if average:
generate_output_file(os.path.join(path_tmp, b0_mean_name + ext), fname_b0_mean, verbose=verbose)
generate_output_file(os.path.join(path_tmp, dwi_mean_name + ext), fname_dwi_mean, verbose=verbose)
# Remove temporary files
if remove_temp_files == 1:
printv('\nRemove temporary files...', verbose)
rmtree(path_tmp, verbose=verbose)
# display elapsed time
elapsed_time = time.time() - start_time
printv('\nFinished! Elapsed time: ' + str(int(np.round(elapsed_time))) + 's', verbose)
return fname_b0, fname_b0_mean, fname_dwi, fname_dwi_mean
def dmri_moco(param):
file_data = 'dmri.nii'
file_data_dirname, file_data_basename, file_data_ext = sct.extract_fname(file_data)
file_b0 = 'b0.nii'
file_dwi = 'dwi.nii'
ext_data = '.nii.gz' # workaround "too many open files" by slurping the data
mat_final = 'mat_final/'
file_dwi_group = 'dwi_averaged_groups.nii'
ext_mat = 'Warp.nii.gz' # warping field
# Get dimensions of data
sct.printv('\nGet dimensions of data...', param.verbose)
im_data = Image(file_data)
nx, ny, nz, nt, px, py, pz, pt = im_data.dim
sct.printv(' ' + str(nx) + ' x ' + str(ny) + ' x ' + str(nz), param.verbose)
# Identify b=0 and DWI images
index_b0, index_dwi, nb_b0, nb_dwi = sct_dmri_separate_b0_and_dwi.identify_b0('bvecs.txt', param.fname_bvals, param.bval_min, param.verbose)
# check if dmri and bvecs are the same size
if not nb_b0 + nb_dwi == nt:
sct.printv('\nERROR in ' + os.path.basename(__file__) + ': Size of data (' + str(nt) + ') and size of bvecs (' + str(nb_b0 + nb_dwi) + ') are not the same. Check your bvecs file.\n', 1, 'error')
sys.exit(2)
# Prepare NIFTI (mean/groups...)
#===================================================================================================================
# Split into T dimension
sct.printv('\nSplit along T dimension...', param.verbose)
im_data_split_list = split_data(im_data, 3)
for im in im_data_split_list:
x_dirname, x_basename, x_ext = sct.extract_fname(im.absolutepath)
im.absolutepath = os.path.join(x_dirname, x_basename + ".nii.gz")
im.save()
# Merge b=0 images
sct.printv('\nMerge b=0...', param.verbose)
im_b0_list = []
for it in range(nb_b0):
im_b0_list.append(im_data_split_list[index_b0[it]])
im_b0_out = concat_data(im_b0_list, 3).save(file_b0)
sct.printv((' File created: ' + file_b0), param.verbose)
# Average b=0 images
sct.printv('\nAverage b=0...', param.verbose)
file_b0_mean = sct.add_suffix(file_b0, '_mean')
sct.run(['sct_maths', '-i', file_b0, '-o', file_b0_mean, '-mean', 't'], param.verbose)
# Number of DWI groups
nb_groups = int(math.floor(nb_dwi / param.group_size))
# Generate groups indexes
group_indexes = []
for iGroup in range(nb_groups):
group_indexes.append(index_dwi[(iGroup * param.group_size):((iGroup + 1) * param.group_size)])
# add the remaining images to the last DWI group
nb_remaining = nb_dwi%param.group_size # number of remaining images
if nb_remaining > 0:
nb_groups += 1
group_indexes.append(index_dwi[len(index_dwi) - nb_remaining:len(index_dwi)])
file_dwi_dirname, file_dwi_basename, file_dwi_ext = sct.extract_fname(file_dwi)
# DWI groups
file_dwi_mean = []
for iGroup in tqdm(range(nb_groups), unit='iter', unit_scale=False, desc="Merge within groups", ascii=True, ncols=80):
# get index
index_dwi_i = group_indexes[iGroup]
nb_dwi_i = len(index_dwi_i)
# Merge DW Images
file_dwi_merge_i = os.path.join(file_dwi_dirname, file_dwi_basename + '_' + str(iGroup) + ext_data)
im_dwi_list = []
for it in range(nb_dwi_i):
im_dwi_list.append(im_data_split_list[index_dwi_i[it]])
im_dwi_out = concat_data(im_dwi_list, 3).save(file_dwi_merge_i)
# Average DW Images
file_dwi_mean_i = os.path.join(file_dwi_dirname, file_dwi_basename + '_mean_' + str(iGroup) + ext_data)
file_dwi_mean.append(file_dwi_mean_i)
sct.run(["sct_maths", "-i", file_dwi_merge_i, "-o", file_dwi_mean[iGroup], "-mean", "t"], 0)
# Merge DWI groups means
sct.printv('\nMerging DW files...', param.verbose)
# file_dwi_groups_means_merge = 'dwi_averaged_groups'
im_dw_list = []
for iGroup in range(nb_groups):
im_dw_list.append(file_dwi_mean[iGroup])
im_dw_out = concat_data(im_dw_list, 3).save(file_dwi_group)
# Average DW Images
# TODO: USEFULL ???
sct.printv('\nAveraging all DW images...', param.verbose)
sct.run(["sct_maths", "-i", file_dwi_group, "-o", file_dwi_group + '_mean' + ext_data, "-mean", "t"], param.verbose)
# segment dwi images using otsu algorithm
if param.otsu:
sct.printv('\nSegment group DWI using OTSU algorithm...', param.verbose)
# import module
otsu = importlib.import_module('sct_otsu')
# get class from module
param_otsu = otsu.param() #getattr(otsu, param)
param_otsu.fname_data = file_dwi_group
param_otsu.threshold = param.otsu
param_otsu.file_suffix = '_seg'
# run otsu
otsu.otsu(param_otsu)
file_dwi_group = file_dwi_group + '_seg.nii'
# START MOCO
#===================================================================================================================
# Estimate moco on b0 groups
sct.printv('\n-------------------------------------------------------------------------------', param.verbose)
sct.printv(' Estimating motion on b=0 images...', param.verbose)
sct.printv('-------------------------------------------------------------------------------', param.verbose)
param_moco = param
param_moco.file_data = 'b0.nii'
# identify target image
if index_dwi[0] != 0:
# If first DWI is not the first volume (most common), then there is a least one b=0 image before. In that case
# select it as the target image for registration of all b=0
param_moco.file_target = os.path.join(file_data_dirname, file_data_basename + '_T' + str(index_b0[index_dwi[0] - 1]).zfill(4) + ext_data)
else:
# If first DWI is the first volume, then the target b=0 is the first b=0 from the index_b0.
param_moco.file_target = os.path.join(file_data_dirname, file_data_basename + '_T' + str(index_b0[0]).zfill(4) + ext_data)
param_moco.path_out = ''
param_moco.todo = 'estimate'
param_moco.mat_moco = 'mat_b0groups'
file_mat_b0 = moco.moco(param_moco)
# Estimate moco on dwi groups
sct.printv('\n-------------------------------------------------------------------------------', param.verbose)
sct.printv(' Estimating motion on DW images...', param.verbose)
sct.printv('-------------------------------------------------------------------------------', param.verbose)
param_moco.file_data = file_dwi_group
param_moco.file_target = file_dwi_mean[0] # target is the first DW image (closest to the first b=0)
param_moco.path_out = ''
param_moco.todo = 'estimate_and_apply'
param_moco.mat_moco = 'mat_dwigroups'
file_mat_dwi = moco.moco(param_moco)
# create final mat folder
sct.create_folder(mat_final)
# Copy b=0 registration matrices
# TODO: use file_mat_b0 and file_mat_dwi instead of the hardcoding below
sct.printv('\nCopy b=0 registration matrices...', param.verbose)
for it in range(nb_b0):
sct.copy('mat_b0groups/' + 'mat.Z0000T' + str(it).zfill(4) + ext_mat,
mat_final + 'mat.Z0000T' + str(index_b0[it]).zfill(4) + ext_mat)
# Copy DWI registration matrices
sct.printv('\nCopy DWI registration matrices...', param.verbose)
for iGroup in range(nb_groups):
for dwi in range(len(group_indexes[iGroup])): # we cannot use enumerate because group_indexes has 2 dim.
sct.copy('mat_dwigroups/' + 'mat.Z0000T' + str(iGroup).zfill(4) + ext_mat,
mat_final + 'mat.Z0000T' + str(group_indexes[iGroup][dwi]).zfill(4) + ext_mat)
# Spline Regularization along T
if param.spline_fitting:
moco.spline(mat_final, nt, nz, param.verbose, np.array(index_b0), param.plot_graph)
# combine Eddy Matrices
if param.run_eddy:
param.mat_2_combine = 'mat_eddy'
param.mat_final = mat_final
moco.combine_matrix(param)
# Apply moco on all dmri data
sct.printv('\n-------------------------------------------------------------------------------', param.verbose)
sct.printv(' Apply moco', param.verbose)
sct.printv('-------------------------------------------------------------------------------', param.verbose)
param_moco.file_data = file_data
param_moco.file_target = os.path.join(file_dwi_dirname, file_dwi_basename + '_mean_' + str(0) + ext_data) # reference for reslicing into proper coordinate system
param_moco.path_out = ''
param_moco.mat_moco = mat_final
param_moco.todo = 'apply'
moco.moco(param_moco)
# copy geometric information from header
# NB: this is required because WarpImageMultiTransform in 2D mode wrongly sets pixdim(3) to "1".
im_dmri = Image(file_data)
fname_data_moco = os.path.join(file_data_dirname, file_data_basename + param.suffix + '.nii')
im_dmri_moco = Image(fname_data_moco)
im_dmri_moco.header = im_dmri.header
im_dmri_moco.save()
return os.path.abspath(fname_data_moco)
def moco(param):
# retrieve parameters
fsloutput = 'export FSLOUTPUTTYPE=NIFTI; ' # for faster processing, all outputs are in NIFTI
file_data = param.file_data
file_target = param.file_target
folder_mat = sct.slash_at_the_end(param.mat_moco, 1) # output folder of mat file
todo = param.todo
suffix = param.suffix
#file_schedule = param.file_schedule
verbose = param.verbose
ext = '.nii'
# get path of the toolbox
status, path_sct = commands.getstatusoutput('echo $SCT_DIR')
# print arguments
sct.printv('\nInput parameters:', param.verbose)
sct.printv(' Input file ............'+file_data, param.verbose)
sct.printv(' Reference file ........'+file_target, param.verbose)
sct.printv(' Polynomial degree .....'+param.param[0], param.verbose)
sct.printv(' Smoothing kernel ......'+param.param[1], param.verbose)
sct.printv(' Gradient step .........'+param.param[2], param.verbose)
sct.printv(' Metric ................'+param.param[3], param.verbose)
sct.printv(' Todo ..................'+todo, param.verbose)
sct.printv(' Mask .................'+param.fname_mask, param.verbose)
sct.printv(' Output mat folder .....'+folder_mat, param.verbose)
# create folder for mat files
sct.create_folder(folder_mat)
# Get size of data
sct.printv('\nGet dimensions data...', verbose)
data_im = Image(file_data+ext)
nx, ny, nz, nt, px, py, pz, pt = data_im.dim
sct.printv(('.. '+str(nx)+' x '+str(ny)+' x '+str(nz)+' x '+str(nt)), verbose)
# copy file_target to a temporary file
sct.printv('\nCopy file_target to a temporary file...', verbose)
sct.run('cp '+file_target+ext+' target.nii')
file_target = 'target'
# Split data along T dimension
sct.printv('\nSplit data along T dimension...', verbose)
data_split_list = split_data(data_im, dim=3)
for im in data_split_list:
im.save()
file_data_splitT = file_data + '_T'
# Motion correction: initialization
index = np.arange(nt)
file_data_splitT_num = []
file_data_splitT_moco_num = []
failed_transfo = [0 for i in range(nt)]
file_mat = [[] for i in range(nt)]
# Motion correction: Loop across T
for indice_index in range(nt):
# create indices and display stuff
it = index[indice_index]
file_data_splitT_num.append(file_data_splitT + str(it).zfill(4))
file_data_splitT_moco_num.append(file_data + suffix + '_T' + str(it).zfill(4))
sct.printv(('\nVolume '+str((it))+'/'+str(nt-1)+':'), verbose)
file_mat[it] = folder_mat + 'mat.T' + str(it)
# run 3D registration
failed_transfo[it] = register(param, file_data_splitT_num[it], file_target, file_mat[it], file_data_splitT_moco_num[it])
# average registered volume with target image
# N.B. use weighted averaging: (target * nb_it + moco) / (nb_it + 1)
if param.iterative_averaging and indice_index < 10 and failed_transfo[it] == 0:
sct.run('sct_maths -i '+file_target+ext+' -mul '+str(indice_index+1)+' -o '+file_target+ext)
sct.run('sct_maths -i '+file_target+ext+' -add '+file_data_splitT_moco_num[it]+ext+' -o '+file_target+ext)
sct.run('sct_maths -i '+file_target+ext+' -div '+str(indice_index+2)+' -o '+file_target+ext)
# Replace failed transformation with the closest good one
sct.printv(('\nReplace failed transformations...'), verbose)
fT = [i for i, j in enumerate(failed_transfo) if j == 1]
gT = [i for i, j in enumerate(failed_transfo) if j == 0]
for it in range(len(fT)):
abs_dist = [abs(gT[i]-fT[it]) for i in range(len(gT))]
if not abs_dist == []:
index_good = abs_dist.index(min(abs_dist))
sct.printv(' transfo #'+str(fT[it])+' --> use transfo #'+str(gT[index_good]), verbose)
# copy transformation
sct.run('cp '+file_mat[gT[index_good]]+'Warp.nii.gz'+' '+file_mat[fT[it]]+'Warp.nii.gz')
# apply transformation
sct.run('sct_apply_transfo -i '+file_data_splitT_num[fT[it]]+'.nii -d '+file_target+'.nii -w '+file_mat[fT[it]]+'Warp.nii.gz'+' -o '+file_data_splitT_moco_num[fT[it]]+'.nii'+' -x '+param.interp, verbose)
else:
# exit program if no transformation exists.
sct.printv('\nERROR in '+os.path.basename(__file__)+': No good transformation exist. Exit program.\n', verbose, 'error')
sys.exit(2)
# Merge data along T
file_data_moco = file_data+suffix
if todo != 'estimate':
sct.printv('\nMerge data back along T...', verbose)
from sct_image import concat_data
# im_list = []
fname_list = []
for indice_index in range(len(index)):
# im_list.append(Image(file_data_splitT_moco_num[indice_index] + ext))
fname_list.append(file_data_splitT_moco_num[indice_index] + ext)
im_out = concat_data(fname_list, 3)
im_out.setFileName(file_data_moco + ext)
im_out.save()
# delete file target.nii (to avoid conflict if this function is run another time)
sct.printv('\nRemove temporary file...', verbose)
#os.remove('target.nii')
sct.run('rm target.nii')
def apply(self):
# Initialization
fname_src = self.input_filename # source image (moving)
fname_warp_list = self.warp_input # list of warping fields
fname_out = self.output_filename # output
fname_dest = self.fname_dest # destination image (fix)
verbose = self.verbose
remove_temp_files = self.remove_temp_files
crop_reference = self.crop # if = 1, put 0 everywhere around warping field, if = 2, real crop
interp = sct.get_interpolation('isct_antsApplyTransforms', self.interp)
# Parse list of warping fields
sct.printv('\nParse list of warping fields...', verbose)
use_inverse = []
fname_warp_list_invert = []
# fname_warp_list = fname_warp_list.replace(' ', '') # remove spaces
# fname_warp_list = fname_warp_list.split(",") # parse with comma
for idx_warp, path_warp in enumerate(fname_warp_list):
# Check if inverse matrix is specified with '-' at the beginning of file name
if path_warp.startswith("-"):
use_inverse.append('-i')
fname_warp_list[idx_warp] = path_warp[1:] # remove '-'
fname_warp_list_invert += [[use_inverse[idx_warp], fname_warp_list[idx_warp]]]
else:
use_inverse.append('')
fname_warp_list_invert += [[path_warp]]
path_warp = fname_warp_list[idx_warp]
if path_warp.endswith((".nii", ".nii.gz")) \
and msct_image.Image(fname_warp_list[idx_warp]).header.get_intent()[0] != 'vector':
raise ValueError("Displacement field in {} is invalid: should be encoded" \
" in a 5D file with vector intent code" \
" (see https://nifti.nimh.nih.gov/pub/dist/src/niftilib/nifti1.h" \
.format(path_warp))
# need to check if last warping field is an affine transfo
isLastAffine = False
path_fname, file_fname, ext_fname = sct.extract_fname(fname_warp_list_invert[-1][-1])
if ext_fname in ['.txt', '.mat']:
isLastAffine = True
# check if destination file is 3d
if not sct.check_if_3d(fname_dest):
sct.printv('ERROR: Destination data must be 3d')
# N.B. Here we take the inverse of the warp list, because sct_WarpImageMultiTransform concatenates in the reverse order
fname_warp_list_invert.reverse()
fname_warp_list_invert = functools.reduce(lambda x,y: x+y, fname_warp_list_invert)
# Extract path, file and extension
path_src, file_src, ext_src = sct.extract_fname(fname_src)
path_dest, file_dest, ext_dest = sct.extract_fname(fname_dest)
# Get output folder and file name
if fname_out == '':
path_out = '' # output in user's current directory
file_out = file_src + '_reg'
ext_out = ext_src
fname_out = os.path.join(path_out, file_out + ext_out)
# Get dimensions of data
sct.printv('\nGet dimensions of data...', verbose)
img_src = msct_image.Image(fname_src)
nx, ny, nz, nt, px, py, pz, pt = img_src.dim
# nx, ny, nz, nt, px, py, pz, pt = sct.get_dimension(fname_src)
sct.printv(' ' + str(nx) + ' x ' + str(ny) + ' x ' + str(nz) + ' x ' + str(nt), verbose)
# if 3d
if nt == 1:
# Apply transformation
sct.printv('\nApply transformation...', verbose)
if nz in [0, 1]:
dim = '2'
else:
dim = '3'
sct.run(['isct_antsApplyTransforms',
'-d', dim,
'-i', fname_src,
'-o', fname_out,
'-t',
] + fname_warp_list_invert + [
'-r', fname_dest,
] + interp, verbose=verbose, is_sct_binary=True)
# if 4d, loop across the T dimension
else:
path_tmp = sct.tmp_create(basename="apply_transfo", verbose=verbose)
# convert to nifti into temp folder
sct.printv('\nCopying input data to tmp folder and convert to nii...', verbose)
img_src.save(os.path.join(path_tmp, "data.nii"))
sct.copy(fname_dest, os.path.join(path_tmp, file_dest + ext_dest))
fname_warp_list_tmp = []
for fname_warp in fname_warp_list:
path_warp, file_warp, ext_warp = sct.extract_fname(fname_warp)
sct.copy(fname_warp, os.path.join(path_tmp, file_warp + ext_warp))
fname_warp_list_tmp.append(file_warp + ext_warp)
fname_warp_list_invert_tmp = fname_warp_list_tmp[::-1]
curdir = os.getcwd()
os.chdir(path_tmp)
# split along T dimension
sct.printv('\nSplit along T dimension...', verbose)
im_dat = msct_image.Image('data.nii')
im_header = im_dat.hdr
data_split_list = sct_image.split_data(im_dat, 3)
for im in data_split_list:
im.save()
# apply transfo
sct.printv('\nApply transformation to each 3D volume...', verbose)
for it in range(nt):
file_data_split = 'data_T' + str(it).zfill(4) + '.nii'
file_data_split_reg = 'data_reg_T' + str(it).zfill(4) + '.nii'
status, output = sct.run(['isct_antsApplyTransforms',
'-d', '3',
'-i', file_data_split,
'-o', file_data_split_reg,
'-t',
] + fname_warp_list_invert_tmp + [
'-r', file_dest + ext_dest,
] + interp, verbose, is_sct_binary=True)
# Merge files back
sct.printv('\nMerge file back...', verbose)
import glob
path_out, name_out, ext_out = sct.extract_fname(fname_out)
# im_list = [Image(file_name) for file_name in glob.glob('data_reg_T*.nii')]
# concat_data use to take a list of image in input, now takes a list of file names to open the files one by one (see issue #715)
fname_list = glob.glob('data_reg_T*.nii')
fname_list.sort()
im_out = sct_image.concat_data(fname_list, 3, im_header['pixdim'])
im_out.save(name_out + ext_out)
os.chdir(curdir)
sct.generate_output_file(os.path.join(path_tmp, name_out + ext_out), fname_out)
# Delete temporary folder if specified
if int(remove_temp_files):
sct.printv('\nRemove temporary files...', verbose)
sct.rmtree(path_tmp, verbose=verbose)
# 2. crop the resulting image using dimensions from the warping field
warping_field = fname_warp_list_invert[-1]
# if last warping field is an affine transfo, we need to compute the space of the concatenate warping field:
if isLastAffine:
sct.printv('WARNING: the resulting image could have wrong apparent results. You should use an affine transformation as last transformation...', verbose, 'warning')
elif crop_reference == 1:
ImageCropper(input_file=fname_out, output_file=fname_out, ref=warping_field, background=0).crop()
# sct.run('sct_crop_image -i '+fname_out+' -o '+fname_out+' -ref '+warping_field+' -b 0')
elif crop_reference == 2:
ImageCropper(input_file=fname_out, output_file=fname_out, ref=warping_field).crop()
# sct.run('sct_crop_image -i '+fname_out+' -o '+fname_out+' -ref '+warping_field)
sct.display_viewer_syntax([fname_dest, fname_out], verbose=verbose)
def fmri_moco(param):
file_data = 'fmri'
ext_data = '.nii'
mat_final = 'mat_final/'
ext_mat = 'Warp.nii.gz' # warping field
# Get dimensions of data
sct.printv('\nGet dimensions of data...', param.verbose)
im_data = Image(file_data + '.nii')
nx, ny, nz, nt, px, py, pz, pt = im_data.dim
sct.printv(' ' + str(nx) + ' x ' + str(ny) + ' x ' + str(nz) + ' x ' + str(nt), param.verbose)
# Get orientation
sct.printv('\nData orientation: ' + im_data.orientation, param.verbose)
if im_data.orientation[2] in 'LR':
param.is_sagittal = True
sct.printv(' Treated as sagittal')
elif im_data.orientation[2] in 'IS':
param.is_sagittal = False
sct.printv(' Treated as axial')
else:
param.is_sagittal = False
sct.printv('WARNING: Orientation seems to be neither axial nor sagittal.')
# Adjust group size in case of sagittal scan
if param.is_sagittal and param.group_size != 1:
sct.printv('For sagittal data group_size should be one for more robustness. Forcing group_size=1.', 1, 'warning')
param.group_size = 1
# Split into T dimension
sct.printv('\nSplit along T dimension...', param.verbose)
im_data = Image(file_data + ext_data)
im_data_split_list = split_data(im_data, 3)
for im in im_data_split_list:
im.save()
# assign an index to each volume
index_fmri = list(range(0, nt))
# Number of groups
nb_groups = int(math.floor(nt / param.group_size))
# Generate groups indexes
group_indexes = []
for iGroup in range(nb_groups):
group_indexes.append(index_fmri[(iGroup * param.group_size):((iGroup + 1) * param.group_size)])
# add the remaining images to the last fMRI group
nb_remaining = nt%param.group_size # number of remaining images
if nb_remaining > 0:
nb_groups += 1
group_indexes.append(index_fmri[len(index_fmri) - nb_remaining:len(index_fmri)])
# groups
for iGroup in tqdm(range(nb_groups), unit='iter', unit_scale=False, desc="Merge within groups", ascii=True, ncols=80):
# get index
index_fmri_i = group_indexes[iGroup]
nt_i = len(index_fmri_i)
# Merge Images
file_data_merge_i = file_data + '_' + str(iGroup)
# cmd = fsloutput + 'fslmerge -t ' + file_data_merge_i
# for it in range(nt_i):
# cmd = cmd + ' ' + file_data + '_T' + str(index_fmri_i[it]).zfill(4)
im_fmri_list = []
for it in range(nt_i):
im_fmri_list.append(im_data_split_list[index_fmri_i[it]])
im_fmri_concat = concat_data(im_fmri_list, 3, squeeze_data=True).save(file_data_merge_i + ext_data)
file_data_mean = file_data + '_mean_' + str(iGroup)
if param.group_size == 1:
# copy to new file name instead of averaging (faster)
# note: this is a bandage. Ideally we should skip this entire for loop if g=1
sct.copy(file_data_merge_i + '.nii', file_data_mean + '.nii')
else:
# Average Images
sct.run(['sct_maths', '-i', file_data_merge_i + '.nii', '-o', file_data_mean + '.nii', '-mean', 't'], verbose=0)
# if not average_data_across_dimension(file_data_merge_i+'.nii', file_data_mean+'.nii', 3):
# sct.printv('ERROR in average_data_across_dimension', 1, 'error')
# cmd = fsloutput + 'fslmaths ' + file_data_merge_i + ' -Tmean ' + file_data_mean
# sct.run(cmd, param.verbose)
# Merge groups means. The output 4D volume will be used for motion correction.
sct.printv('\nMerging volumes...', param.verbose)
file_data_groups_means_merge = 'fmri_averaged_groups'
im_mean_list = []
for iGroup in range(nb_groups):
im_mean_list.append(Image(file_data + '_mean_' + str(iGroup) + ext_data))
im_mean_concat = concat_data(im_mean_list, 3).save(file_data_groups_means_merge + ext_data)
# Estimate moco
sct.printv('\n-------------------------------------------------------------------------------', param.verbose)
sct.printv(' Estimating motion...', param.verbose)
sct.printv('-------------------------------------------------------------------------------', param.verbose)
param_moco = param
param_moco.file_data = 'fmri_averaged_groups'
param_moco.file_target = file_data + '_mean_' + param.num_target
param_moco.path_out = ''
param_moco.todo = 'estimate_and_apply'
param_moco.mat_moco = 'mat_groups'
file_mat = moco.moco(param_moco)
# TODO: if g=1, no need to run the block below (already applied)
if param.group_size == 1:
# if flag g=1, it means that all images have already been corrected, so we just need to rename the file
sct.mv('fmri_averaged_groups_moco.nii', 'fmri_moco.nii')
else:
# create final mat folder
sct.create_folder(mat_final)
# Copy registration matrices
sct.printv('\nCopy transformations...', param.verbose)
for iGroup in range(nb_groups):
for data in range(len(group_indexes[iGroup])): # we cannot use enumerate because group_indexes has 2 dim.
# fetch all file_mat_z for given t-group
list_file_mat_z = file_mat[:, iGroup]
# loop across file_mat_z and copy to mat_final folder
for file_mat_z in list_file_mat_z:
# we want to copy 'mat_groups/mat.ZXXXXTYYYYWarp.nii.gz' --> 'mat_final/mat.ZXXXXTYYYZWarp.nii.gz'
# Notice the Y->Z in the under the T index: the idea here is to use the single matrix from each group,
# and apply it to all images belonging to the same group.
sct.copy(file_mat_z + ext_mat,
mat_final + file_mat_z[11:20] + 'T' + str(group_indexes[iGroup][data]).zfill(4) + ext_mat)
# Apply moco on all fmri data
sct.printv('\n-------------------------------------------------------------------------------', param.verbose)
sct.printv(' Apply moco', param.verbose)
sct.printv('-------------------------------------------------------------------------------', param.verbose)
param_moco.file_data = 'fmri'
param_moco.file_target = file_data + '_mean_' + str(0)
param_moco.path_out = ''
param_moco.mat_moco = mat_final
param_moco.todo = 'apply'
moco.moco(param_moco)
# copy geometric information from header
# NB: this is required because WarpImageMultiTransform in 2D mode wrongly sets pixdim(3) to "1".
im_fmri = Image('fmri.nii')
im_fmri_moco = Image('fmri_moco.nii')
im_fmri_moco.header = im_fmri.header
im_fmri_moco.save()
# Average volumes
sct.printv('\nAveraging data...', param.verbose)
sct_maths.main(args=['-i', 'fmri_moco.nii',
'-o', 'fmri_moco_mean.nii',
'-mean', 't',
'-v', '0'])
def main(fname_anat, fname_centerline, degree_poly, centerline_fitting, interp, remove_temp_files, verbose):
# extract path of the script
path_script = os.path.dirname(__file__)+'/'
# Parameters for debug mode
if param.debug == 1:
print '\n*** WARNING: DEBUG MODE ON ***\n'
status, path_sct_data = commands.getstatusoutput('echo $SCT_TESTING_DATA_DIR')
fname_anat = path_sct_data+'/t2/t2.nii.gz'
fname_centerline = path_sct_data+'/t2/t2_seg.nii.gz'
# extract path/file/extension
path_anat, file_anat, ext_anat = sct.extract_fname(fname_anat)
# Display arguments
print '\nCheck input arguments...'
print ' Input volume ...................... '+fname_anat
print ' Centerline ........................ '+fname_centerline
print ''
# Get input image orientation
im_anat = Image(fname_anat)
input_image_orientation = get_orientation(im_anat)
# Reorient input data into RL PA IS orientation
im_centerline = Image(fname_centerline)
im_anat_orient = set_orientation(im_anat, 'RPI')
im_anat_orient.setFileName('tmp.anat_orient.nii')
im_centerline_orient = set_orientation(im_centerline, 'RPI')
im_centerline_orient.setFileName('tmp.centerline_orient.nii')
# Open centerline
#==========================================================================================
print '\nGet dimensions of input centerline...'
nx, ny, nz, nt, px, py, pz, pt = im_centerline_orient.dim
print '.. matrix size: '+str(nx)+' x '+str(ny)+' x '+str(nz)
print '.. voxel size: '+str(px)+'mm x '+str(py)+'mm x '+str(pz)+'mm'
print '\nOpen centerline volume...'
data = im_centerline_orient.data
X, Y, Z = (data>0).nonzero()
min_z_index, max_z_index = min(Z), max(Z)
# loop across z and associate x,y coordinate with the point having maximum intensity
x_centerline = [0 for iz in range(min_z_index, max_z_index+1, 1)]
y_centerline = [0 for iz in range(min_z_index, max_z_index+1, 1)]
z_centerline = [iz for iz in range(min_z_index, max_z_index+1, 1)]
# Two possible scenario:
# 1. the centerline is probabilistic: each slices contains voxels with the probability of containing the centerline [0:...:1]
# We only take the maximum value of the image to aproximate the centerline.
# 2. The centerline/segmentation image contains many pixels per slice with values {0,1}.
# We take all the points and approximate the centerline on all these points.
X, Y, Z = ((data<1)*(data>0)).nonzero() # X is empty if binary image
if (len(X) > 0): # Scenario 1
for iz in range(min_z_index, max_z_index+1, 1):
x_centerline[iz-min_z_index], y_centerline[iz-min_z_index] = numpy.unravel_index(data[:,:,iz].argmax(), data[:,:,iz].shape)
else: # Scenario 2
for iz in range(min_z_index, max_z_index+1, 1):
x_seg, y_seg = (data[:,:,iz]>0).nonzero()
if len(x_seg) > 0:
x_centerline[iz-min_z_index] = numpy.mean(x_seg)
y_centerline[iz-min_z_index] = numpy.mean(y_seg)
# TODO: find a way to do the previous loop with this, which is more neat:
# [numpy.unravel_index(data[:,:,iz].argmax(), data[:,:,iz].shape) for iz in range(0,nz,1)]
# clear variable
del data
# Fit the centerline points with the kind of curve given as argument of the script and return the new smoothed coordinates
if centerline_fitting == 'nurbs':
try:
x_centerline_fit, y_centerline_fit = b_spline_centerline(x_centerline,y_centerline,z_centerline)
except ValueError:
print "splines fitting doesn't work, trying with polynomial fitting...\n"
x_centerline_fit, y_centerline_fit = polynome_centerline(x_centerline,y_centerline,z_centerline)
elif centerline_fitting == 'polynome':
x_centerline_fit, y_centerline_fit = polynome_centerline(x_centerline,y_centerline,z_centerline)
#==========================================================================================
# Split input volume
print '\nSplit input volume...'
im_anat_orient_split_list = split_data(im_anat_orient, 2)
file_anat_split = []
for im in im_anat_orient_split_list:
file_anat_split.append(im.absolutepath)
im.save()
# initialize variables
file_mat_inv_cumul = ['tmp.mat_inv_cumul_Z'+str(z).zfill(4) for z in range(0,nz,1)]
z_init = min_z_index
displacement_max_z_index = x_centerline_fit[z_init-min_z_index]-x_centerline_fit[max_z_index-min_z_index]
# write centerline as text file
print '\nGenerate fitted transformation matrices...'
file_mat_inv_cumul_fit = ['tmp.mat_inv_cumul_fit_Z'+str(z).zfill(4) for z in range(0,nz,1)]
for iz in range(min_z_index, max_z_index+1, 1):
# compute inverse cumulative fitted transformation matrix
fid = open(file_mat_inv_cumul_fit[iz], 'w')
if (x_centerline[iz-min_z_index] == 0 and y_centerline[iz-min_z_index] == 0):
displacement = 0
else:
displacement = x_centerline_fit[z_init-min_z_index]-x_centerline_fit[iz-min_z_index]
fid.write('%i %i %i %f\n' %(1, 0, 0, displacement) )
fid.write('%i %i %i %f\n' %(0, 1, 0, 0) )
fid.write('%i %i %i %i\n' %(0, 0, 1, 0) )
fid.write('%i %i %i %i\n' %(0, 0, 0, 1) )
fid.close()
# we complete the displacement matrix in z direction
for iz in range(0, min_z_index, 1):
fid = open(file_mat_inv_cumul_fit[iz], 'w')
fid.write('%i %i %i %f\n' %(1, 0, 0, 0) )
fid.write('%i %i %i %f\n' %(0, 1, 0, 0) )
fid.write('%i %i %i %i\n' %(0, 0, 1, 0) )
fid.write('%i %i %i %i\n' %(0, 0, 0, 1) )
fid.close()
for iz in range(max_z_index+1, nz, 1):
fid = open(file_mat_inv_cumul_fit[iz], 'w')
fid.write('%i %i %i %f\n' %(1, 0, 0, displacement_max_z_index) )
fid.write('%i %i %i %f\n' %(0, 1, 0, 0) )
fid.write('%i %i %i %i\n' %(0, 0, 1, 0) )
fid.write('%i %i %i %i\n' %(0, 0, 0, 1) )
fid.close()
# apply transformations to data
print '\nApply fitted transformation matrices...'
file_anat_split_fit = ['tmp.anat_orient_fit_Z'+str(z).zfill(4) for z in range(0,nz,1)]
for iz in range(0, nz, 1):
# forward cumulative transformation to data
sct.run(fsloutput+'flirt -in '+file_anat_split[iz]+' -ref '+file_anat_split[iz]+' -applyxfm -init '+file_mat_inv_cumul_fit[iz]+' -out '+file_anat_split_fit[iz]+' -interp '+interp)
# Merge into 4D volume
print '\nMerge into 4D volume...'
from glob import glob
im_to_concat_list = [Image(fname) for fname in glob('tmp.anat_orient_fit_Z*.nii')]
im_concat_out = concat_data(im_to_concat_list, 2)
im_concat_out.setFileName('tmp.anat_orient_fit.nii')
im_concat_out.save()
# sct.run(fsloutput+'fslmerge -z tmp.anat_orient_fit tmp.anat_orient_fit_z*')
# Reorient data as it was before
print '\nReorient data back into native orientation...'
fname_anat_fit_orient = set_orientation(im_concat_out.absolutepath, input_image_orientation, filename=True)
move(fname_anat_fit_orient, 'tmp.anat_orient_fit_reorient.nii')
# Generate output file (in current folder)
print '\nGenerate output file (in current folder)...'
sct.generate_output_file('tmp.anat_orient_fit_reorient.nii', file_anat+'_flatten'+ext_anat)
# Delete temporary files
if remove_temp_files == 1:
print '\nDelete temporary files...'
sct.run('rm -rf tmp.*')
# to view results
print '\nDone! To view results, type:'
print 'fslview '+file_anat+ext_anat+' '+file_anat+'_flatten'+ext_anat+' &\n'
def register2d(fname_src, fname_dest, fname_mask='', fname_warp='warp_forward.nii.gz', fname_warp_inv='warp_inverse.nii.gz', paramreg=Paramreg(step='0', type='im', algo='Translation', metric='MI', iter='5', shrink='1', smooth='0', gradStep='0.5'),
ants_registration_params={'rigid': '', 'affine': '', 'compositeaffine': '', 'similarity': '', 'translation': '','bspline': ',10', 'gaussiandisplacementfield': ',3,0',
'bsplinedisplacementfield': ',5,10', 'syn': ',3,0', 'bsplinesyn': ',1,3'}, verbose=0):
"""Slice-by-slice registration of two images.
We first split the 3D images into 2D images (and the mask if inputted). Then we register slices of the two images
that physically correspond to one another looking at the physical origin of each image. The images can be of
different sizes but the destination image must be smaller thant the input image. We do that using antsRegistration
in 2D. Once this has been done for each slices, we gather the results and return them.
Algorithms implemented: translation, rigid, affine, syn and BsplineSyn.
N.B.: If the mask is inputted, it must also be 3D and it must be in the same space as the destination image.
input:
fname_source: name of moving image (type: string)
fname_dest: name of fixed image (type: string)
mask[optional]: name of mask file (type: string) (parameter -x of antsRegistration)
fname_warp: name of output 3d forward warping field
fname_warp_inv: name of output 3d inverse warping field
paramreg[optional]: parameters of antsRegistration (type: Paramreg class from sct_register_multimodal)
ants_registration_params[optional]: specific algorithm's parameters for antsRegistration (type: dictionary)
output:
if algo==translation:
x_displacement: list of translation along x axis for each slice (type: list)
y_displacement: list of translation along y axis for each slice (type: list)
if algo==rigid:
x_displacement: list of translation along x axis for each slice (type: list)
y_displacement: list of translation along y axis for each slice (type: list)
theta_rotation: list of rotation angle in radian (and in ITK's coordinate system) for each slice (type: list)
if algo==affine or algo==syn or algo==bsplinesyn:
creation of two 3D warping fields (forward and inverse) that are the concatenations of the slice-by-slice
warps.
"""
# set metricSize
if paramreg.metric == 'MI':
metricSize = '32' # corresponds to number of bins
else:
metricSize = '4' # corresponds to radius (for CC, MeanSquares...)
# Get image dimensions and retrieve nz
sct.printv('\nGet image dimensions of destination image...', verbose)
nx, ny, nz, nt, px, py, pz, pt = Image(fname_dest).dim
sct.printv('.. matrix size: '+str(nx)+' x '+str(ny)+' x '+str(nz), verbose)
sct.printv('.. voxel size: '+str(px)+'mm x '+str(py)+'mm x '+str(pz)+'mm', verbose)
# Split input volume along z
sct.printv('\nSplit input volume...', verbose)
from sct_image import split_data
im_src = Image('src.nii')
split_source_list = split_data(im_src, 2)
for im in split_source_list:
im.save()
# Split destination volume along z
sct.printv('\nSplit destination volume...', verbose)
im_dest = Image('dest.nii')
split_dest_list = split_data(im_dest, 2)
for im in split_dest_list:
im.save()
# Split mask volume along z
if fname_mask != '':
sct.printv('\nSplit mask volume...', verbose)
im_mask = Image('mask.nii.gz')
split_mask_list = split_data(im_mask, 2)
for im in split_mask_list:
im.save()
# coord_origin_dest = im_dest.transfo_pix2phys([[0,0,0]])
# coord_origin_input = im_src.transfo_pix2phys([[0,0,0]])
# coord_diff_origin = (np.asarray(coord_origin_dest[0]) - np.asarray(coord_origin_input[0])).tolist()
# [x_o, y_o, z_o] = [coord_diff_origin[0] * 1.0/px, coord_diff_origin[1] * 1.0/py, coord_diff_origin[2] * 1.0/pz]
# initialization
if paramreg.algo in ['Translation']:
x_displacement = [0 for i in range(nz)]
y_displacement = [0 for i in range(nz)]
theta_rotation = [0 for i in range(nz)]
if paramreg.algo in ['Rigid', 'Affine', 'BSplineSyN', 'SyN']:
list_warp = []
list_warp_inv = []
# loop across slices
for i in range(nz):
# set masking
sct.printv('Registering slice '+str(i)+'/'+str(nz-1)+'...', verbose)
num = numerotation(i)
prefix_warp2d = 'warp2d_'+num
# if mask is used, prepare command for ANTs
if fname_mask != '':
masking = '-x mask_Z' +num+ '.nii.gz'
else:
masking = ''
# main command for registration
cmd = ('isct_antsRegistration '
'--dimensionality 2 '
'--transform '+paramreg.algo+'['+str(paramreg.gradStep) +
ants_registration_params[paramreg.algo.lower()]+'] '
'--metric '+paramreg.metric+'[dest_Z' + num + '.nii' + ',src_Z' + num + '.nii' +',1,'+metricSize+'] ' #[fixedImage,movingImage,metricWeight +nb_of_bins (MI) or radius (other)
'--convergence '+str(paramreg.iter)+' '
'--shrink-factors '+str(paramreg.shrink)+' '
'--smoothing-sigmas '+str(paramreg.smooth)+'mm '
'--output ['+prefix_warp2d+',src_Z'+ num +'_reg.nii] ' #--> file.mat (contains Tx,Ty, theta)
'--interpolation BSpline[3] '
+ masking)
# add init translation
if not paramreg.init == '':
init_dict = {'geometric': '0', 'centermass': '1', 'origin': '2'}
cmd += ' -r [dest_Z'+num+'.nii'+',src_Z'+num+'.nii,'+init_dict[paramreg.init]+']'
try:
# run registration
sct.run(cmd)
if paramreg.algo in ['Translation']:
file_mat = prefix_warp2d+'0GenericAffine.mat'
matfile = loadmat(file_mat, struct_as_record=True)
array_transfo = matfile['AffineTransform_double_2_2']
x_displacement[i] = array_transfo[4][0] # Tx in ITK'S coordinate system
y_displacement[i] = array_transfo[5][0] # Ty in ITK'S and fslview's coordinate systems
theta_rotation[i] = asin(array_transfo[2]) # angle of rotation theta in ITK'S coordinate system (minus theta for fslview)
if paramreg.algo in ['Rigid', 'Affine', 'BSplineSyN', 'SyN']:
# List names of 2d warping fields for subsequent merge along Z
file_warp2d = prefix_warp2d+'0Warp.nii.gz'
file_warp2d_inv = prefix_warp2d+'0InverseWarp.nii.gz'
list_warp.append(file_warp2d)
list_warp_inv.append(file_warp2d_inv)
if paramreg.algo in ['Rigid', 'Affine']:
# Generating null 2d warping field (for subsequent concatenation with affine transformation)
sct.run('isct_antsRegistration -d 2 -t SyN[1, 1, 1] -c 0 -m MI[dest_Z'+num+'.nii, src_Z'+num+'.nii, 1, 32] -o warp2d_null -f 1 -s 0')
# --> outputs: warp2d_null0Warp.nii.gz, warp2d_null0InverseWarp.nii.gz
file_mat = prefix_warp2d + '0GenericAffine.mat'
# Concatenating mat transfo and null 2d warping field to obtain 2d warping field of affine transformation
sct.run('isct_ComposeMultiTransform 2 ' + file_warp2d + ' -R dest_Z'+num+'.nii warp2d_null0Warp.nii.gz ' + file_mat)
sct.run('isct_ComposeMultiTransform 2 ' + file_warp2d_inv + ' -R src_Z'+num+'.nii warp2d_null0InverseWarp.nii.gz -i ' + file_mat)
# if an exception occurs with ants, take the last value for the transformation
# TODO: DO WE NEED TO DO THAT??? (julien 2016-03-01)
except Exception, e:
sct.printv('ERROR: Exception occurred.\n'+str(e), 1, 'error')
def main(args=None):
if not args:
args = sys.argv[1:]
# initialize parameters
param = Param()
# call main function
parser = get_parser()
arguments = parser.parse(args)
fname_data = arguments['-i']
fname_bvecs = arguments['-bvec']
average = arguments['-a']
verbose = int(arguments['-v'])
remove_tmp_files = int(arguments['-r'])
path_out = arguments['-ofolder']
if '-bval' in arguments:
fname_bvals = arguments['-bval']
else:
fname_bvals = ''
if '-bvalmin' in arguments:
param.bval_min = arguments['-bvalmin']
# Initialization
start_time = time.time()
# sct.printv(arguments)
sct.printv('\nInput parameters:', verbose)
sct.printv(' input file ............' + fname_data, verbose)
sct.printv(' bvecs file ............' + fname_bvecs, verbose)
sct.printv(' bvals file ............' + fname_bvals, verbose)
sct.printv(' average ...............' + str(average), verbose)
# Get full path
fname_data = os.path.abspath(fname_data)
fname_bvecs = os.path.abspath(fname_bvecs)
if fname_bvals:
fname_bvals = os.path.abspath(fname_bvals)
# Extract path, file and extension
path_data, file_data, ext_data = sct.extract_fname(fname_data)
# # get output folder
# if path_out == '':
# path_out = ''
# create temporary folder
sct.printv('\nCreate temporary folder...', verbose)
path_tmp = sct.slash_at_the_end('tmp.' + time.strftime("%y%m%d%H%M%S"), 1)
sct.run('mkdir ' + path_tmp, verbose)
# copy files into tmp folder and convert to nifti
sct.printv('\nCopy files into temporary folder...', verbose)
ext = '.nii'
dmri_name = 'dmri'
b0_name = 'b0'
b0_mean_name = b0_name + '_mean'
dwi_name = 'dwi'
dwi_mean_name = dwi_name + '_mean'
from sct_convert import convert
if not convert(fname_data, path_tmp + dmri_name + ext):
sct.printv('ERROR in convert.', 1, 'error')
sct.run('cp ' + fname_bvecs + ' ' + path_tmp + 'bvecs', verbose)
# go to tmp folder
os.chdir(path_tmp)
# Get size of data
im_dmri = Image(dmri_name + ext)
sct.printv('\nGet dimensions data...', verbose)
nx, ny, nz, nt, px, py, pz, pt = im_dmri.dim
sct.printv('.. ' + str(nx) + ' x ' + str(ny) + ' x ' + str(nz) + ' x ' + str(nt), verbose)
# Identify b=0 and DWI images
sct.printv(fname_bvals)
index_b0, index_dwi, nb_b0, nb_dwi = identify_b0(fname_bvecs, fname_bvals, param.bval_min, verbose)
# Split into T dimension
sct.printv('\nSplit along T dimension...', verbose)
im_dmri_split_list = split_data(im_dmri, 3)
for im_d in im_dmri_split_list:
im_d.save()
# Merge b=0 images
sct.printv('\nMerge b=0...', verbose)
cmd = 'sct_image -concat t -o ' + b0_name + ext + ' -i '
for it in range(nb_b0):
cmd = cmd + dmri_name + '_T' + str(index_b0[it]).zfill(4) + ext + ','
cmd = cmd[:-1] # remove ',' at the end of the string
# WARNING: calling concat_data in python instead of in command line causes a non understood issue
status, output = sct.run(cmd, param.verbose)
# Average b=0 images
if average:
sct.printv('\nAverage b=0...', verbose)
sct.run('sct_maths -i ' + b0_name + ext + ' -o ' + b0_mean_name + ext + ' -mean t', verbose)
# Merge DWI
cmd = 'sct_image -concat t -o ' + dwi_name + ext + ' -i '
for it in range(nb_dwi):
cmd = cmd + dmri_name + '_T' + str(index_dwi[it]).zfill(4) + ext + ','
cmd = cmd[:-1] # remove ',' at the end of the string
# WARNING: calling concat_data in python instead of in command line causes a non understood issue
status, output = sct.run(cmd, param.verbose)
# Average DWI images
if average:
sct.printv('\nAverage DWI...', verbose)
sct.run('sct_maths -i ' + dwi_name + ext + ' -o ' + dwi_mean_name + ext + ' -mean t', verbose)
# if not average_data_across_dimension('dwi.nii', 'dwi_mean.nii', 3):
# sct.printv('ERROR in average_data_across_dimension', 1, 'error')
# sct.run(fsloutput + 'fslmaths dwi -Tmean dwi_mean', verbose)
# come back to parent folder
os.chdir('..')
# Generate output files
sct.printv('\nGenerate output files...', verbose)
sct.generate_output_file(path_tmp + b0_name + ext, path_out + b0_name + ext_data, verbose)
sct.generate_output_file(path_tmp + dwi_name + ext, path_out + dwi_name + ext_data, verbose)
if average:
sct.generate_output_file(path_tmp + b0_mean_name + ext, path_out + b0_mean_name + ext_data, verbose)
sct.generate_output_file(path_tmp + dwi_mean_name + ext, path_out + dwi_mean_name + ext_data, verbose)
# Remove temporary files
if remove_tmp_files == 1:
sct.printv('\nRemove temporary files...', verbose)
sct.run('rm -rf ' + path_tmp, verbose)
# display elapsed time
elapsed_time = time.time() - start_time
sct.printv('\nFinished! Elapsed time: ' + str(int(round(elapsed_time))) + 's', verbose)
# to view results
sct.printv('\nTo view results, type: ', verbose)
if average:
sct.printv('fslview b0 b0_mean dwi dwi_mean &\n', verbose)
else:
sct.printv('fslview b0 dwi &\n', verbose)
def main(args=None):
if not args:
args = sys.argv[1:]
# initialize parameters
param = Param()
# call main function
parser = get_parser()
arguments = parser.parse(args)
fname_data = arguments['-i']
fname_bvecs = arguments['-bvec']
average = arguments['-a']
verbose = int(arguments.get('-v'))
sct.init_sct(log_level=verbose, update=True) # Update log level
remove_temp_files = int(arguments['-r'])
path_out = arguments['-ofolder']
if '-bval' in arguments:
fname_bvals = arguments['-bval']
else:
fname_bvals = ''
if '-bvalmin' in arguments:
param.bval_min = arguments['-bvalmin']
# Initialization
start_time = time.time()
# sct.printv(arguments)
sct.printv('\nInput parameters:', verbose)
sct.printv(' input file ............' + fname_data, verbose)
sct.printv(' bvecs file ............' + fname_bvecs, verbose)
sct.printv(' bvals file ............' + fname_bvals, verbose)
sct.printv(' average ...............' + str(average), verbose)
# Get full path
fname_data = os.path.abspath(fname_data)
fname_bvecs = os.path.abspath(fname_bvecs)
if fname_bvals:
fname_bvals = os.path.abspath(fname_bvals)
# Extract path, file and extension
path_data, file_data, ext_data = sct.extract_fname(fname_data)
# create temporary folder
path_tmp = sct.tmp_create(basename="dmri_separate", verbose=verbose)
# copy files into tmp folder and convert to nifti
sct.printv('\nCopy files into temporary folder...', verbose)
ext = '.nii'
dmri_name = 'dmri'
b0_name = file_data + '_b0'
b0_mean_name = b0_name + '_mean'
dwi_name = file_data + '_dwi'
dwi_mean_name = dwi_name + '_mean'
if not convert(fname_data, os.path.join(path_tmp, dmri_name + ext)):
sct.printv('ERROR in convert.', 1, 'error')
sct.copy(fname_bvecs, os.path.join(path_tmp, "bvecs"), verbose=verbose)
# go to tmp folder
curdir = os.getcwd()
os.chdir(path_tmp)
# Get size of data
im_dmri = Image(dmri_name + ext)
sct.printv('\nGet dimensions data...', verbose)
nx, ny, nz, nt, px, py, pz, pt = im_dmri.dim
sct.printv('.. ' + str(nx) + ' x ' + str(ny) + ' x ' + str(nz) + ' x ' + str(nt), verbose)
# Identify b=0 and DWI images
sct.printv(fname_bvals)
index_b0, index_dwi, nb_b0, nb_dwi = identify_b0(fname_bvecs, fname_bvals, param.bval_min, verbose)
# Split into T dimension
sct.printv('\nSplit along T dimension...', verbose)
im_dmri_split_list = split_data(im_dmri, 3)
for im_d in im_dmri_split_list:
im_d.save()
# Merge b=0 images
sct.printv('\nMerge b=0...', verbose)
from sct_image import concat_data
l = []
for it in range(nb_b0):
l.append(dmri_name + '_T' + str(index_b0[it]).zfill(4) + ext)
im_out = concat_data(l, 3).save(b0_name + ext)
# Average b=0 images
if average:
sct.printv('\nAverage b=0...', verbose)
sct.run(['sct_maths', '-i', b0_name + ext, '-o', b0_mean_name + ext, '-mean', 't'], verbose)
# Merge DWI
l = []
for it in range(nb_dwi):
l.append(dmri_name + '_T' + str(index_dwi[it]).zfill(4) + ext)
im_out = concat_data(l, 3).save(dwi_name + ext)
# Average DWI images
if average:
sct.printv('\nAverage DWI...', verbose)
sct.run(['sct_maths', '-i', dwi_name + ext, '-o', dwi_mean_name + ext, '-mean', 't'], verbose)
# come back
os.chdir(curdir)
# Generate output files
fname_b0 = os.path.abspath(os.path.join(path_out, b0_name + ext_data))
fname_dwi = os.path.abspath(os.path.join(path_out, dwi_name + ext_data))
fname_b0_mean = os.path.abspath(os.path.join(path_out, b0_mean_name + ext_data))
fname_dwi_mean = os.path.abspath(os.path.join(path_out, dwi_mean_name + ext_data))
sct.printv('\nGenerate output files...', verbose)
sct.generate_output_file(os.path.join(path_tmp, b0_name + ext), fname_b0, verbose)
sct.generate_output_file(os.path.join(path_tmp, dwi_name + ext), fname_dwi, verbose)
if average:
sct.generate_output_file(os.path.join(path_tmp, b0_mean_name + ext), fname_b0_mean, verbose)
sct.generate_output_file(os.path.join(path_tmp, dwi_mean_name + ext), fname_dwi_mean, verbose)
# Remove temporary files
if remove_temp_files == 1:
sct.printv('\nRemove temporary files...', verbose)
sct.rmtree(path_tmp, verbose=verbose)
# display elapsed time
elapsed_time = time.time() - start_time
sct.printv('\nFinished! Elapsed time: ' + str(int(np.round(elapsed_time))) + 's', verbose)
return fname_b0, fname_b0_mean, fname_dwi, fname_dwi_mean
def apply(self):
# Initialization
fname_src = self.input_filename # source image (moving)
fname_warp_list = self.warp_input # list of warping fields
fname_out = self.output_filename # output
fname_dest = self.fname_dest # destination image (fix)
verbose = self.verbose
remove_temp_files = self.remove_temp_files
crop_reference = self.crop # if = 1, put 0 everywhere around warping field, if = 2, real crop
interp = sct.get_interpolation('isct_antsApplyTransforms', self.interp)
# Parse list of warping fields
sct.printv('\nParse list of warping fields...', verbose)
use_inverse = []
fname_warp_list_invert = []
# fname_warp_list = fname_warp_list.replace(' ', '') # remove spaces
# fname_warp_list = fname_warp_list.split(",") # parse with comma
for i in range(len(fname_warp_list)):
# Check if inverse matrix is specified with '-' at the beginning of file name
if fname_warp_list[i].find('-') == 0:
use_inverse.append('-i ')
fname_warp_list[i] = fname_warp_list[i][1:] # remove '-'
else:
use_inverse.append('')
sct.printv(' Transfo #'+str(i)+': '+use_inverse[i]+fname_warp_list[i], verbose)
fname_warp_list_invert.append(use_inverse[i]+fname_warp_list[i])
# need to check if last warping field is an affine transfo
isLastAffine = False
path_fname, file_fname, ext_fname = sct.extract_fname(fname_warp_list_invert[-1])
if ext_fname in ['.txt', '.mat']:
isLastAffine = True
# Check file existence
sct.printv('\nCheck file existence...', verbose)
sct.check_file_exist(fname_src, self.verbose)
sct.check_file_exist(fname_dest, self.verbose)
for i in range(len(fname_warp_list)):
# check if file exist
sct.check_file_exist(fname_warp_list[i], self.verbose)
# check if destination file is 3d
if not sct.check_if_3d(fname_dest):
sct.printv('ERROR: Destination data must be 3d')
# N.B. Here we take the inverse of the warp list, because sct_WarpImageMultiTransform concatenates in the reverse order
fname_warp_list_invert.reverse()
# Extract path, file and extension
path_src, file_src, ext_src = sct.extract_fname(fname_src)
path_dest, file_dest, ext_dest = sct.extract_fname(fname_dest)
# Get output folder and file name
if fname_out == '':
path_out = '' # output in user's current directory
file_out = file_src+'_reg'
ext_out = ext_src
fname_out = path_out+file_out+ext_out
# Get dimensions of data
sct.printv('\nGet dimensions of data...', verbose)
from msct_image import Image
nx, ny, nz, nt, px, py, pz, pt = Image(fname_src).dim
# nx, ny, nz, nt, px, py, pz, pt = sct.get_dimension(fname_src)
sct.printv(' ' + str(nx) + ' x ' + str(ny) + ' x ' + str(nz)+ ' x ' + str(nt), verbose)
# if 3d
if nt == 1:
# Apply transformation
sct.printv('\nApply transformation...', verbose)
sct.run('isct_antsApplyTransforms -d 3 -i '+fname_src+' -o '+fname_out+' -t '+' '.join(fname_warp_list_invert)+' -r '+fname_dest+interp, verbose)
# if 4d, loop across the T dimension
else:
# create temporary folder
sct.printv('\nCreate temporary folder...', verbose)
path_tmp = sct.slash_at_the_end('tmp.'+time.strftime("%y%m%d%H%M%S"), 1)
# sct.run('mkdir '+path_tmp, verbose)
sct.run('mkdir '+path_tmp, verbose)
# convert to nifti into temp folder
sct.printv('\nCopying input data to tmp folder and convert to nii...', verbose)
from sct_convert import convert
convert(fname_src, path_tmp+'data.nii')
sct.run('cp '+fname_dest+' '+path_tmp+file_dest+ext_dest)
fname_warp_list_tmp = []
for fname_warp in fname_warp_list:
path_warp, file_warp, ext_warp = sct.extract_fname(fname_warp)
sct.run('cp '+fname_warp+' '+path_tmp+file_warp+ext_warp)
fname_warp_list_tmp.append(file_warp+ext_warp)
fname_warp_list_invert_tmp = fname_warp_list_tmp[::-1]
os.chdir(path_tmp)
# split along T dimension
sct.printv('\nSplit along T dimension...', verbose)
from sct_image import split_data
im_dat = Image('data.nii')
data_split_list = split_data(im_dat, 3)
for im in data_split_list:
im.save()
# apply transfo
sct.printv('\nApply transformation to each 3D volume...', verbose)
for it in range(nt):
file_data_split = 'data_T'+str(it).zfill(4)+'.nii'
file_data_split_reg = 'data_reg_T'+str(it).zfill(4)+'.nii'
sct.run('isct_antsApplyTransforms -d 3 -i '+file_data_split+' -o '+file_data_split_reg+' -t '+' '.join(fname_warp_list_invert_tmp)+' -r '+file_dest+ext_dest+interp, verbose)
# Merge files back
sct.printv('\nMerge file back...', verbose)
from sct_image import concat_data
import glob
path_out, name_out, ext_out = sct.extract_fname(fname_out)
im_list = [Image(file_name) for file_name in glob.glob('data_reg_T*.nii')]
im_out = concat_data(im_list, 3)
im_out.setFileName(name_out+ext_out)
im_out.save()
os.chdir('..')
sct.generate_output_file(path_tmp+name_out+ext_out, fname_out)
# Delete temporary folder if specified
if int(remove_temp_files):
sct.printv('\nRemove temporary files...', verbose)
sct.run('rm -rf '+path_tmp, verbose, error_exit='warning')
# 2. crop the resulting image using dimensions from the warping field
warping_field = fname_warp_list_invert[-1]
# if last warping field is an affine transfo, we need to compute the space of the concatenate warping field:
if isLastAffine:
sct.printv('WARNING: the resulting image could have wrong apparent results. You should use an affine transformation as last transformation...',verbose,'warning')
elif crop_reference == 1:
ImageCropper(input_file=fname_out, output_file=fname_out, ref=warping_field, background=0).crop()
# sct.run('sct_crop_image -i '+fname_out+' -o '+fname_out+' -ref '+warping_field+' -b 0')
elif crop_reference == 2:
ImageCropper(input_file=fname_out, output_file=fname_out, ref=warping_field).crop()
# sct.run('sct_crop_image -i '+fname_out+' -o '+fname_out+' -ref '+warping_field)
# display elapsed time
sct.printv('\nDone! To view results, type:', verbose)
sct.printv('fslview '+fname_dest+' '+fname_out+' &\n', verbose, 'info')
