def dummy_segmentation_4d(vol_num=10, create_bvecs=False, size_arr=(256, 256, 256), pixdim=(1, 1, 1), dtype=np.float64,
orientation='LPI', shape='rectangle', angle_RL=0, angle_AP=0, angle_IS=0, radius_RL=5.0,
radius_AP=3.0, degree=2, interleaved=False, zeroslice=[], debug=False):
"""
Create a dummy 4D segmentation (dMRI/fMRI) and dummy bvecs file (optional)
:param vol_num: int: number of volumes in 4D data
:param create_bvecs: bool: create dummy bvecs file (necessary e.g. for sct_dmri_moco)
other parameters are same as in dummy_segmentation function
:return: Image object
"""
img_list = []
# Loop across individual volumes of 4D data
for volume in range(0,vol_num):
# set debug=True in line below for saving individual volumes into individual nii files
img_list.append(dummy_segmentation(size_arr=size_arr, pixdim=pixdim, dtype=dtype, orientation=orientation,
shape=shape, angle_RL=angle_RL, angle_AP=angle_AP, angle_IS=angle_IS,
radius_RL=radius_RL, radius_AP=radius_AP, degree=degree, zeroslice=zeroslice,
interleaved=interleaved, debug=False))
# Concatenate individual 3D images into 4D data
img_4d = concat_data(img_list, 3)
if debug:
out_name = datetime.now().strftime("%Y%m%d%H%M%S%f")
file_4d_data = 'tmp_dummy_4d_' + out_name + '.nii.gz'
img_4d.save(file_4d_data, verbose=0)
# Create a dummy bvecs file (necessary e.g. for sct_dmri_moco)
if create_bvecs:
n_b0 = 1 # number of b0
n_dwi = vol_num-n_b0 # number of dwi
bvecs_dummy = ['', '', '']
bvec_b0 = np.array([[0.0, 0.0, 0.0]] * n_b0)
bvec_dwi = np.array([[uniform(0,1), uniform(0,1), uniform(0,1)]] * n_dwi)
bvec = np.concatenate((bvec_b0,bvec_dwi),axis=0)
# Concatenate bvecs
for i in (0, 1, 2):
bvecs_dummy[i] += ' '.join(str(v) for v in map(lambda n: '%.16f' % n, bvec[:, i]))
bvecs_dummy[i] += ' '
bvecs_concat = '\n'.join(str(v) for v in bvecs_dummy) # transform list into lines of strings
if debug:
new_f = open('tmp_dummy_4d_' + out_name + '.bvec', 'w')
new_f.write(bvecs_concat)
new_f.close()
return img_4d
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 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 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 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 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 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 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 create_mask(param):
# parse argument for method
method_type = param.process[0]
# check method val
if not method_type == 'center':
method_val = param.process[1]
# check existence of input files
if method_type == 'centerline':
sct.check_file_exist(method_val, param.verbose)
# Extract path/file/extension
path_data, file_data, ext_data = sct.extract_fname(param.fname_data)
# Get output folder and file name
if param.fname_out == '':
param.fname_out = os.path.abspath(param.file_prefix + file_data + ext_data)
path_tmp = sct.tmp_create(basename="create_mask", verbose=param.verbose)
sct.printv('\nOrientation:', param.verbose)
orientation_input = Image(param.fname_data).orientation
sct.printv(' ' + orientation_input, param.verbose)
# copy input data to tmp folder and re-orient to RPI
Image(param.fname_data).change_orientation("RPI").save(os.path.join(path_tmp, "data_RPI.nii"))
if method_type == 'centerline':
Image(method_val).change_orientation("RPI").save(os.path.join(path_tmp, "centerline_RPI.nii"))
if method_type == 'point':
Image(method_val).change_orientation("RPI").save(os.path.join(path_tmp, "point_RPI.nii"))
# go to tmp folder
curdir = os.getcwd()
os.chdir(path_tmp)
# Get dimensions of data
im_data = Image('data_RPI.nii')
nx, ny, nz, nt, px, py, pz, pt = im_data.dim
sct.printv('\nDimensions:', param.verbose)
sct.printv(im_data.dim, param.verbose)
# in case user input 4d data
if nt != 1:
sct.printv('WARNING in ' + os.path.basename(__file__) + ': Input image is 4d but output mask will be 3D from first time slice.', param.verbose, 'warning')
# extract first volume to have 3d reference
nii = msct_image.empty_like(Image('data_RPI.nii'))
data3d = nii.data[:, :, :, 0]
nii.data = data3d
nii.save('data_RPI.nii')
if method_type == 'coord':
# parse to get coordinate
coord = [x for x in map(int, method_val.split('x'))]
if method_type == 'point':
# get file name
# extract coordinate of point
sct.printv('\nExtract coordinate of point...', param.verbose)
# TODO: change this way to remove dependence to sct.run. ProcessLabels.display_voxel returns list of coordinates
status, output = sct.run(['sct_label_utils', '-i', 'point_RPI.nii', '-display'], verbose=param.verbose)
# parse to get coordinate
# TODO fixup... this is quite magic
coord = output[output.find('Position=') + 10:-17].split(',')
if method_type == 'center':
# set coordinate at center of FOV
coord = np.round(float(nx) / 2), np.round(float(ny) / 2)
if method_type == 'centerline':
# get name of centerline from user argument
fname_centerline = 'centerline_RPI.nii'
else:
# generate volume with line along Z at coordinates 'coord'
sct.printv('\nCreate line...', param.verbose)
fname_centerline = create_line(param, 'data_RPI.nii', coord, nz)
# create mask
sct.printv('\nCreate mask...', param.verbose)
centerline = nibabel.load(fname_centerline) # open centerline
hdr = centerline.get_header() # get header
hdr.set_data_dtype('uint8') # set imagetype to uint8
spacing = hdr.structarr['pixdim']
data_centerline = centerline.get_data() # get centerline
# if data is 2D, reshape with empty third dimension
if len(data_centerline.shape) == 2:
data_centerline_shape = list(data_centerline.shape)
data_centerline_shape.append(1)
data_centerline = data_centerline.reshape(data_centerline_shape)
z_centerline_not_null = [iz for iz in range(0, nz, 1) if data_centerline[:, :, iz].any()]
# get center of mass of the centerline
cx = [0] * nz
cy = [0] * nz
for iz in range(0, nz, 1):
if iz in z_centerline_not_null:
cx[iz], cy[iz] = ndimage.measurements.center_of_mass(np.array(data_centerline[:, :, iz]))
# create 2d masks
file_mask = 'data_mask'
for iz in range(nz):
if iz not in z_centerline_not_null:
# write an empty nifty volume
img = nibabel.Nifti1Image(data_centerline[:, :, iz], None, hdr)
nibabel.save(img, (file_mask + str(iz) + '.nii'))
else:
center = np.array([cx[iz], cy[iz]])
mask2d = create_mask2d(param, center, param.shape, param.size, im_data=im_data)
# Write NIFTI volumes
img = nibabel.Nifti1Image(mask2d, None, hdr)
nibabel.save(img, (file_mask + str(iz) + '.nii'))
fname_list = [file_mask + str(iz) + '.nii' for iz in range(nz)]
im_out = concat_data(fname_list, dim=2).save('mask_RPI.nii.gz')
im_out.change_orientation(orientation_input)
im_out.header = Image(param.fname_data).header
im_out.save(param.fname_out)
# come back
os.chdir(curdir)
# Remove temporary files
if param.remove_temp_files == 1:
sct.printv('\nRemove temporary files...', param.verbose)
sct.rmtree(path_tmp)
sct.display_viewer_syntax([param.fname_data, param.fname_out], colormaps=['gray', 'red'], opacities=['', '0.5'])
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 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 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 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 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'
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 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 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 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 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 create_mask():
fsloutput = 'export FSLOUTPUTTYPE=NIFTI; ' # for faster processing, all outputs are in NIFTI
# parse argument for method
method_type = param.process[0]
# check method val
if not method_type == 'center':
method_val = param.process[1]
# check existence of input files
if method_type == 'centerline':
sct.check_file_exist(method_val, param.verbose)
# check if orientation is RPI
sct.printv('\nCheck if orientation is RPI...', param.verbose)
ori = get_orientation(param.fname_data, filename=True)
if not ori == 'RPI':
sct.printv('\nERROR in '+os.path.basename(__file__)+': Orientation of input image should be RPI. Use sct_image -setorient to put your image in RPI.\n', 1, 'error')
# Extract path/file/extension
path_data, file_data, ext_data = sct.extract_fname(param.fname_data)
# Get output folder and file name
if param.fname_out == '':
param.fname_out = param.file_prefix+file_data+ext_data
#fname_out = os.path.abspath(path_out+file_out+ext_out)
# create temporary folder
sct.printv('\nCreate temporary folder...', param.verbose)
path_tmp = sct.slash_at_the_end('tmp.'+time.strftime("%y%m%d%H%M%S"), 1)
sct.run('mkdir '+path_tmp, param.verbose)
# Copying input data to tmp folder and convert to nii
sct.printv('\nCopying input data to tmp folder and convert to nii...', param.verbose)
convert(param.fname_data, path_tmp+'data.nii')
# sct.run('cp '+param.fname_data+' '+path_tmp+'data'+ext_data, param.verbose)
if method_type == 'centerline':
convert(method_val, path_tmp+'centerline.nii.gz')
# go to tmp folder
os.chdir(path_tmp)
# Get dimensions of data
sct.printv('\nGet dimensions of data...', param.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), param.verbose)
# in case user input 4d data
if nt != 1:
sct.printv('WARNING in '+os.path.basename(__file__)+': Input image is 4d but output mask will 3D.', param.verbose, 'warning')
# extract first volume to have 3d reference
nii = Image('data.nii')
data3d = nii.data[:,:,:,0]
nii.data = data3d
nii.save()
if method_type == 'coord':
# parse to get coordinate
coord = map(int, method_val.split('x'))
if method_type == 'point':
# get file name
fname_point = method_val
# extract coordinate of point
sct.printv('\nExtract coordinate of point...', param.verbose)
status, output = sct.run('sct_label_utils -i '+fname_point+' -p display-voxel', param.verbose)
# parse to get coordinate
coord = output[output.find('Position=')+10:-17].split(',')
if method_type == 'center':
# set coordinate at center of FOV
coord = round(float(nx)/2), round(float(ny)/2)
if method_type == 'centerline':
# get name of centerline from user argument
fname_centerline = 'centerline.nii.gz'
else:
# generate volume with line along Z at coordinates 'coord'
sct.printv('\nCreate line...', param.verbose)
fname_centerline = create_line('data.nii', coord, nz)
# create mask
sct.printv('\nCreate mask...', param.verbose)
centerline = nibabel.load(fname_centerline) # open centerline
hdr = centerline.get_header() # get header
hdr.set_data_dtype('uint8') # set imagetype to uint8
data_centerline = centerline.get_data() # get centerline
z_centerline = [iz for iz in range(0, nz, 1) if data_centerline[:, :, iz].any()]
nz = len(z_centerline)
# get center of mass of the centerline
cx = [0] * nz
cy = [0] * nz
for iz in range(0, nz, 1):
cx[iz], cy[iz] = ndimage.measurements.center_of_mass(numpy.array(data_centerline[:, :, z_centerline[iz]]))
# create 2d masks
file_mask = 'data_mask'
for iz in range(nz):
center = numpy.array([cx[iz], cy[iz]])
mask2d = create_mask2d(center, param.shape, param.size, nx, ny, param.even)
# Write NIFTI volumes
img = nibabel.Nifti1Image(mask2d, None, hdr)
nibabel.save(img, (file_mask+str(iz)+'.nii'))
# merge along Z
# cmd = 'fslmerge -z mask '
im_list = []
for iz in range(nz):
im_list.append(Image(file_mask+str(iz)+'.nii'))
im_out = concat_data(im_list, 2)
im_out.setFileName('mask.nii.gz')
im_out.save()
# copy geometry
im_dat = Image('data.nii')
im_mask = Image('mask.nii.gz')
im_mask = copy_header(im_dat, im_mask)
im_mask.save()
# come back to parent folder
os.chdir('..')
# Generate output files
sct.printv('\nGenerate output files...', param.verbose)
sct.generate_output_file(path_tmp+'mask.nii.gz', param.fname_out)
# Remove temporary files
if param.remove_tmp_files == 1:
sct.printv('\nRemove temporary files...', param.verbose)
sct.run('rm -rf '+path_tmp, param.verbose, error_exit='warning')
# to view results
sct.printv('\nDone! To view results, type:', param.verbose)
sct.printv('fslview '+param.fname_data+' '+param.fname_out+' -l Red -t 0.5 &', param.verbose, 'info')
print
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 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 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 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 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 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)
# 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 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.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 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 create_mask(param):
# parse argument for method
method_type = param.process[0]
# check method val
if not method_type == 'center':
method_val = param.process[1]
# check existence of input files
if method_type == 'centerline':
sct.check_file_exist(method_val, param.verbose)
# Extract path/file/extension
path_data, file_data, ext_data = sct.extract_fname(param.fname_data)
# Get output folder and file name
if param.fname_out == '':
param.fname_out = os.path.abspath(param.file_prefix + file_data +
ext_data)
path_tmp = sct.tmp_create(basename="create_mask", verbose=param.verbose)
sct.printv('\nOrientation:', param.verbose)
orientation_input = Image(param.fname_data).orientation
sct.printv(' ' + orientation_input, param.verbose)
# copy input data to tmp folder and re-orient to RPI
Image(param.fname_data).change_orientation("RPI").save(
os.path.join(path_tmp, "data_RPI.nii"))
if method_type == 'centerline':
Image(method_val).change_orientation("RPI").save(
os.path.join(path_tmp, "centerline_RPI.nii"))
if method_type == 'point':
Image(method_val).change_orientation("RPI").save(
os.path.join(path_tmp, "point_RPI.nii"))
# go to tmp folder
curdir = os.getcwd()
os.chdir(path_tmp)
# Get dimensions of data
im_data = Image('data_RPI.nii')
nx, ny, nz, nt, px, py, pz, pt = im_data.dim
sct.printv('\nDimensions:', param.verbose)
sct.printv(im_data.dim, param.verbose)
# in case user input 4d data
if nt != 1:
sct.printv(
'WARNING in ' + os.path.basename(__file__) +
': Input image is 4d but output mask will be 3D from first time slice.',
param.verbose, 'warning')
# extract first volume to have 3d reference
nii = msct_image.empty_like(Image('data_RPI.nii'))
data3d = nii.data[:, :, :, 0]
nii.data = data3d
nii.save('data_RPI.nii')
if method_type == 'coord':
# parse to get coordinate
coord = [x for x in map(int, method_val.split('x'))]
if method_type == 'point':
# get file name
# extract coordinate of point
sct.printv('\nExtract coordinate of point...', param.verbose)
# TODO: change this way to remove dependence to sct.run. ProcessLabels.display_voxel returns list of coordinates
status, output = sct.run(
['sct_label_utils', '-i', 'point_RPI.nii', '-display'],
verbose=param.verbose)
# parse to get coordinate
# TODO fixup... this is quite magic
coord = output[output.find('Position=') + 10:-17].split(',')
if method_type == 'center':
# set coordinate at center of FOV
coord = np.round(float(nx) / 2), np.round(float(ny) / 2)
if method_type == 'centerline':
# get name of centerline from user argument
fname_centerline = 'centerline_RPI.nii'
else:
# generate volume with line along Z at coordinates 'coord'
sct.printv('\nCreate line...', param.verbose)
fname_centerline = create_line(param, 'data_RPI.nii', coord, nz)
# create mask
sct.printv('\nCreate mask...', param.verbose)
centerline = nibabel.load(fname_centerline) # open centerline
hdr = centerline.get_header() # get header
hdr.set_data_dtype('uint8') # set imagetype to uint8
spacing = hdr.structarr['pixdim']
data_centerline = centerline.get_data() # get centerline
# if data is 2D, reshape with empty third dimension
if len(data_centerline.shape) == 2:
data_centerline_shape = list(data_centerline.shape)
data_centerline_shape.append(1)
data_centerline = data_centerline.reshape(data_centerline_shape)
z_centerline_not_null = [
iz for iz in range(0, nz, 1) if data_centerline[:, :, iz].any()
]
# get center of mass of the centerline
cx = [0] * nz
cy = [0] * nz
for iz in range(0, nz, 1):
if iz in z_centerline_not_null:
cx[iz], cy[iz] = ndimage.measurements.center_of_mass(
np.array(data_centerline[:, :, iz]))
# create 2d masks
file_mask = 'data_mask'
for iz in range(nz):
if iz not in z_centerline_not_null:
# write an empty nifty volume
img = nibabel.Nifti1Image(data_centerline[:, :, iz], None, hdr)
nibabel.save(img, (file_mask + str(iz) + '.nii'))
else:
center = np.array([cx[iz], cy[iz]])
mask2d = create_mask2d(param,
center,
param.shape,
param.size,
im_data=im_data)
# Write NIFTI volumes
img = nibabel.Nifti1Image(mask2d, None, hdr)
nibabel.save(img, (file_mask + str(iz) + '.nii'))
fname_list = [file_mask + str(iz) + '.nii' for iz in range(nz)]
im_out = concat_data(fname_list, dim=2).save('mask_RPI.nii.gz')
im_out.change_orientation(orientation_input)
im_out.header = Image(param.fname_data).header
im_out.save(param.fname_out)
# come back
os.chdir(curdir)
# Remove temporary files
if param.remove_temp_files == 1:
sct.printv('\nRemove temporary files...', param.verbose)
sct.rmtree(path_tmp)
sct.display_viewer_syntax([param.fname_data, param.fname_out],
colormaps=['gray', 'red'],
opacities=['', '0.5'])
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 create_mask():
fsloutput = 'export FSLOUTPUTTYPE=NIFTI; ' # for faster processing, all outputs are in NIFTI
# parse argument for method
method_type = param.process[0]
# check method val
if not method_type == 'center':
method_val = param.process[1]
# check existence of input files
if method_type == 'centerline':
sct.check_file_exist(method_val, param.verbose)
# Extract path/file/extension
path_data, file_data, ext_data = sct.extract_fname(param.fname_data)
# Get output folder and file name
if param.fname_out == '':
param.fname_out = param.file_prefix+file_data+ext_data
# create temporary folder
sct.printv('\nCreate temporary folder...', param.verbose)
path_tmp = sct.tmp_create(param.verbose)
# )sct.slash_at_the_end('tmp.'+time.strftime("%y%m%d%H%M%S"), 1)
# sct.run('mkdir '+path_tmp, param.verbose)
sct.printv('\nCheck orientation...', param.verbose)
orientation_input = get_orientation(Image(param.fname_data))
sct.printv('.. '+orientation_input, param.verbose)
reorient_coordinates = False
# copy input data to tmp folder
convert(param.fname_data, path_tmp+'data.nii')
if method_type == 'centerline':
convert(method_val, path_tmp+'centerline.nii.gz')
if method_type == 'point':
convert(method_val, path_tmp+'point.nii.gz')
# go to tmp folder
os.chdir(path_tmp)
# reorient to RPI
sct.printv('\nReorient to RPI...', param.verbose)
# if not orientation_input == 'RPI':
sct.run('sct_image -i data.nii -o data_RPI.nii -setorient RPI -v 0', verbose=False)
if method_type == 'centerline':
sct.run('sct_image -i centerline.nii.gz -o centerline_RPI.nii.gz -setorient RPI -v 0', verbose=False)
if method_type == 'point':
sct.run('sct_image -i point.nii.gz -o point_RPI.nii.gz -setorient RPI -v 0', verbose=False)
#
# if method_type == 'centerline':
# orientation_centerline = get_orientation_3d(method_val, filename=True)
# if not orientation_centerline == 'RPI':
# sct.run('sct_image -i ' + method_val + ' -o ' + path_tmp + 'centerline.nii.gz' + ' -setorient RPI -v 0', verbose=False)
# else:
# convert(method_val, path_tmp+'centerline.nii.gz')
# Get dimensions of data
sct.printv('\nGet dimensions of data...', param.verbose)
nx, ny, nz, nt, px, py, pz, pt = Image('data_RPI.nii').dim
sct.printv(' ' + str(nx) + ' x ' + str(ny) + ' x ' + str(nz)+ ' x ' + str(nt), param.verbose)
# in case user input 4d data
if nt != 1:
sct.printv('WARNING in '+os.path.basename(__file__)+': Input image is 4d but output mask will 3D.', param.verbose, 'warning')
# extract first volume to have 3d reference
nii = Image('data_RPI.nii')
data3d = nii.data[:,:,:,0]
nii.data = data3d
nii.save()
if method_type == 'coord':
# parse to get coordinate
coord = map(int, method_val.split('x'))
if method_type == 'point':
# get file name
fname_point = method_val
# extract coordinate of point
sct.printv('\nExtract coordinate of point...', param.verbose)
# TODO: change this way to remove dependence to sct.run. ProcessLabels.display_voxel returns list of coordinates
status, output = sct.run('sct_label_utils -i point_RPI.nii.gz -display', param.verbose)
# parse to get coordinate
coord = output[output.find('Position=')+10:-17].split(',')
if method_type == 'center':
# set coordinate at center of FOV
coord = round(float(nx)/2), round(float(ny)/2)
if method_type == 'centerline':
# get name of centerline from user argument
fname_centerline = 'centerline_RPI.nii.gz'
else:
# generate volume with line along Z at coordinates 'coord'
sct.printv('\nCreate line...', param.verbose)
fname_centerline = create_line('data_RPI.nii', coord, nz)
# create mask
sct.printv('\nCreate mask...', param.verbose)
centerline = nibabel.load(fname_centerline) # open centerline
hdr = centerline.get_header() # get header
hdr.set_data_dtype('uint8') # set imagetype to uint8
spacing = hdr.structarr['pixdim']
data_centerline = centerline.get_data() # get centerline
z_centerline_not_null = [iz for iz in range(0, nz, 1) if data_centerline[:, :, iz].any()]
# get center of mass of the centerline
cx = [0] * nz
cy = [0] * nz
for iz in range(0, nz, 1):
if iz in z_centerline_not_null:
cx[iz], cy[iz] = ndimage.measurements.center_of_mass(numpy.array(data_centerline[:, :, iz]))
# create 2d masks
file_mask = 'data_mask'
for iz in range(nz):
if iz not in z_centerline_not_null:
# write an empty nifty volume
img = nibabel.Nifti1Image(data_centerline[:, :, iz], None, hdr)
nibabel.save(img, (file_mask + str(iz) + '.nii'))
else:
center = numpy.array([cx[iz], cy[iz]])
mask2d = create_mask2d(center, param.shape, param.size, nx, ny, even=param.even, spacing=spacing)
# Write NIFTI volumes
img = nibabel.Nifti1Image(mask2d, None, hdr)
nibabel.save(img, (file_mask+str(iz)+'.nii'))
# merge along Z
# cmd = 'fslmerge -z mask '
# CHANGE THAT CAN IMPACT SPEED:
# related to issue #755, we cannot open more than 256 files at one time.
# to solve this issue, we do not open more than 100 files
'''
im_list = []
im_temp = []
for iz in range(nz_not_null):
if iz != 0 and iz % 100 == 0:
im_temp.append(concat_data(im_list, 2))
im_list = [Image(file_mask + str(iz) + '.nii')]
else:
im_list.append(Image(file_mask+str(iz)+'.nii'))
if im_temp:
im_temp.append(concat_data(im_list, 2))
im_out = concat_data(im_temp, 2, no_expand=True)
else:
im_out = concat_data(im_list, 2)
'''
fname_list = [file_mask + str(iz) + '.nii' for iz in range(nz)]
im_out = concat_data(fname_list, dim=2)
im_out.setFileName('mask_RPI.nii.gz')
im_out.save()
# reorient if necessary
# if not orientation_input == 'RPI':
sct.run('sct_image -i mask_RPI.nii.gz -o mask.nii.gz -setorient ' + orientation_input, param.verbose)
# copy header input --> mask
im_dat = Image('data.nii')
im_mask = Image('mask.nii.gz')
im_mask = copy_header(im_dat, im_mask)
im_mask.save()
# come back to parent folder
os.chdir('..')
# Generate output files
sct.printv('\nGenerate output files...', param.verbose)
sct.generate_output_file(path_tmp+'mask.nii.gz', param.fname_out)
# Remove temporary files
if param.remove_tmp_files == 1:
sct.printv('\nRemove temporary files...', param.verbose)
sct.run('rm -rf '+path_tmp, param.verbose, error_exit='warning')
# to view results
sct.printv('\nDone! To view results, type:', param.verbose)
sct.printv('fslview '+param.fname_data+' '+param.fname_out+' -l Red -t 0.5 &', param.verbose, 'info')
print
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 create_mask():
fsloutput = 'export FSLOUTPUTTYPE=NIFTI; ' # for faster processing, all outputs are in NIFTI
# parse argument for method
method_type = param.process[0]
# check method val
if not method_type == 'center':
method_val = param.process[1]
# check existence of input files
if method_type == 'centerline':
sct.check_file_exist(method_val, param.verbose)
# Extract path/file/extension
path_data, file_data, ext_data = sct.extract_fname(param.fname_data)
# Get output folder and file name
if param.fname_out == '':
param.fname_out = param.file_prefix + file_data + ext_data
# create temporary folder
sct.printv('\nCreate temporary folder...', param.verbose)
path_tmp = sct.tmp_create(param.verbose)
# )sct.slash_at_the_end('tmp.'+time.strftime("%y%m%d%H%M%S"), 1)
# sct.run('mkdir '+path_tmp, param.verbose)
sct.printv('\nCheck orientation...', param.verbose)
orientation_input = get_orientation(Image(param.fname_data))
sct.printv('.. ' + orientation_input, param.verbose)
reorient_coordinates = False
# copy input data to tmp folder
convert(param.fname_data, path_tmp + 'data.nii')
if method_type == 'centerline':
convert(method_val, path_tmp + 'centerline.nii.gz')
if method_type == 'point':
convert(method_val, path_tmp + 'point.nii.gz')
# go to tmp folder
os.chdir(path_tmp)
# reorient to RPI
sct.printv('\nReorient to RPI...', param.verbose)
# if not orientation_input == 'RPI':
sct.run('sct_image -i data.nii -o data_RPI.nii -setorient RPI -v 0',
verbose=False)
if method_type == 'centerline':
sct.run(
'sct_image -i centerline.nii.gz -o centerline_RPI.nii.gz -setorient RPI -v 0',
verbose=False)
if method_type == 'point':
sct.run(
'sct_image -i point.nii.gz -o point_RPI.nii.gz -setorient RPI -v 0',
verbose=False)
#
# if method_type == 'centerline':
# orientation_centerline = get_orientation_3d(method_val, filename=True)
# if not orientation_centerline == 'RPI':
# sct.run('sct_image -i ' + method_val + ' -o ' + path_tmp + 'centerline.nii.gz' + ' -setorient RPI -v 0', verbose=False)
# else:
# convert(method_val, path_tmp+'centerline.nii.gz')
# Get dimensions of data
sct.printv('\nGet dimensions of data...', param.verbose)
nx, ny, nz, nt, px, py, pz, pt = Image('data_RPI.nii').dim
sct.printv(
' ' + str(nx) + ' x ' + str(ny) + ' x ' + str(nz) + ' x ' + str(nt),
param.verbose)
# in case user input 4d data
if nt != 1:
sct.printv(
'WARNING in ' + os.path.basename(__file__) +
': Input image is 4d but output mask will 3D.', param.verbose,
'warning')
# extract first volume to have 3d reference
nii = Image('data_RPI.nii')
data3d = nii.data[:, :, :, 0]
nii.data = data3d
nii.save()
if method_type == 'coord':
# parse to get coordinate
coord = map(int, method_val.split('x'))
if method_type == 'point':
# get file name
fname_point = method_val
# extract coordinate of point
sct.printv('\nExtract coordinate of point...', param.verbose)
# TODO: change this way to remove dependence to sct.run. ProcessLabels.display_voxel returns list of coordinates
status, output = sct.run(
'sct_label_utils -i point_RPI.nii.gz -display', param.verbose)
# parse to get coordinate
coord = output[output.find('Position=') + 10:-17].split(',')
if method_type == 'center':
# set coordinate at center of FOV
coord = round(float(nx) / 2), round(float(ny) / 2)
if method_type == 'centerline':
# get name of centerline from user argument
fname_centerline = 'centerline_RPI.nii.gz'
else:
# generate volume with line along Z at coordinates 'coord'
sct.printv('\nCreate line...', param.verbose)
fname_centerline = create_line('data_RPI.nii', coord, nz)
# create mask
sct.printv('\nCreate mask...', param.verbose)
centerline = nibabel.load(fname_centerline) # open centerline
hdr = centerline.get_header() # get header
hdr.set_data_dtype('uint8') # set imagetype to uint8
spacing = hdr.structarr['pixdim']
data_centerline = centerline.get_data() # get centerline
# if data is 2D, reshape with empty third dimension
if len(data_centerline.shape) == 2:
data_centerline_shape = list(data_centerline.shape)
data_centerline_shape.append(1)
data_centerline = data_centerline.reshape(data_centerline_shape)
z_centerline_not_null = [
iz for iz in range(0, nz, 1) if data_centerline[:, :, iz].any()
]
# get center of mass of the centerline
cx = [0] * nz
cy = [0] * nz
for iz in range(0, nz, 1):
if iz in z_centerline_not_null:
cx[iz], cy[iz] = ndimage.measurements.center_of_mass(
numpy.array(data_centerline[:, :, iz]))
# create 2d masks
file_mask = 'data_mask'
for iz in range(nz):
if iz not in z_centerline_not_null:
# write an empty nifty volume
img = nibabel.Nifti1Image(data_centerline[:, :, iz], None, hdr)
nibabel.save(img, (file_mask + str(iz) + '.nii'))
else:
center = numpy.array([cx[iz], cy[iz]])
mask2d = create_mask2d(center,
param.shape,
param.size,
nx,
ny,
even=param.even,
spacing=spacing)
# Write NIFTI volumes
img = nibabel.Nifti1Image(mask2d, None, hdr)
nibabel.save(img, (file_mask + str(iz) + '.nii'))
# merge along Z
# cmd = 'fslmerge -z mask '
# CHANGE THAT CAN IMPACT SPEED:
# related to issue #755, we cannot open more than 256 files at one time.
# to solve this issue, we do not open more than 100 files
'''
im_list = []
im_temp = []
for iz in range(nz_not_null):
if iz != 0 and iz % 100 == 0:
im_temp.append(concat_data(im_list, 2))
im_list = [Image(file_mask + str(iz) + '.nii')]
else:
im_list.append(Image(file_mask+str(iz)+'.nii'))
if im_temp:
im_temp.append(concat_data(im_list, 2))
im_out = concat_data(im_temp, 2, no_expand=True)
else:
im_out = concat_data(im_list, 2)
'''
fname_list = [file_mask + str(iz) + '.nii' for iz in range(nz)]
im_out = concat_data(fname_list, dim=2)
im_out.setFileName('mask_RPI.nii.gz')
im_out.save()
# reorient if necessary
# if not orientation_input == 'RPI':
sct.run(
'sct_image -i mask_RPI.nii.gz -o mask.nii.gz -setorient ' +
orientation_input, param.verbose)
# copy header input --> mask
im_dat = Image('data.nii')
im_mask = Image('mask.nii.gz')
im_mask = copy_header(im_dat, im_mask)
im_mask.save()
# come back to parent folder
os.chdir('..')
# Generate output files
sct.printv('\nGenerate output files...', param.verbose)
sct.generate_output_file(path_tmp + 'mask.nii.gz', param.fname_out)
# Remove temporary files
if param.remove_tmp_files == 1:
sct.printv('\nRemove temporary files...', param.verbose)
sct.run('rm -rf ' + path_tmp, param.verbose, error_exit='warning')
# to view results
sct.printv('\nDone! To view results, type:', param.verbose)
sct.printv(
'fslview ' + param.fname_data + ' ' + param.fname_out +
' -l Red -t 0.5 &', param.verbose, 'info')
print
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')
