def test_add_suffix():
assert msct_image.add_suffix('t2.nii', '_mean') == 't2_mean.nii'
assert msct_image.add_suffix('t2.nii.gz', 'a') == 't2a.nii.gz'
assert msct_image.add_suffix(os.path.join('var', 'lib', 'usr', 't2.nii.gz'), 'sfx') \
== os.path.join('var', 'lib', 'usr', 't2sfx.nii.gz')
assert msct_image.add_suffix(os.path.join('var', 'lib.version.3', 'usr', 't2.nii.gz'), 'sfx') \
== os.path.join('var', 'lib.version.3', 'usr', 't2sfx.nii.gz')
def test_add_suffix():
assert msct_image.add_suffix('t2.nii', '_mean') == 't2_mean.nii'
assert msct_image.add_suffix('t2.nii.gz', 'a') == 't2a.nii.gz'
assert msct_image.add_suffix('var/lib/usr/t2.nii.gz',
'sfx') == 'var/lib/usr/t2sfx.nii.gz'
assert msct_image.add_suffix('var/lib.version.3/usr/t2.nii.gz',
'sfx') == 'var/lib.version.3/usr/t2sfx.nii.gz'
def resample_image(fname, suffix='_resampled.nii.gz', binary=False, npx=0.3, npy=0.3, thr=0.0, interpolation='spline'):
"""
Resampling function: add a padding, resample, crop the padding
:param fname: name of the image file to be resampled
:param suffix: suffix added to the original fname after resampling
:param binary: boolean, image is binary or not
:param npx: new pixel size in the x direction
:param npy: new pixel size in the y direction
:param thr: if the image is binary, it will be thresholded at thr (default=0) after the resampling
:param interpolation: type of interpolation used for the resampling
:return: file name after resampling (or original fname if it was already in the correct resolution)
"""
im_in = Image(fname)
orientation = im_in.orientation
if orientation != 'RPI':
im_in.change_orientation('RPI')
fname = add_suffix(im_in.absolutepath, "_rpi")
im_in.save(path=fname, mutable=True)
nx, ny, nz, nt, px, py, pz, pt = im_in.dim
if np.round(px, 2) != np.round(npx, 2) or np.round(py, 2) != np.round(npy, 2):
name_resample = extract_fname(fname)[1] + suffix
if binary:
interpolation = 'nn'
if nz == 1:
# when data is 2d: we convert it to a 3d image in order to avoid conversion problem with 2d data
# TODO: check if this above problem is still present (now that we are using nibabel instead of nipy)
run_proc(['sct_image', '-i', ','.join([fname, fname]), '-concat', 'z', '-o', fname])
run_proc(['sct_resample', '-i', fname, '-mm', str(npx) + 'x' + str(npy) + 'x' + str(pz), '-o', name_resample, '-x', interpolation])
if nz == 1: # when input data was 2d: re-convert data 3d-->2d
run_proc(['sct_image', '-i', name_resample, '-split', 'z'])
im_split = Image(name_resample.split('.nii.gz')[0] + '_Z0000.nii.gz')
im_split.save(name_resample)
if binary:
img = Image(name_resample)
img.data = binarize(img.data, thr)
img.save()
if orientation != 'RPI':
img = Image(name_resample)
img.change_orientation(orientation)
name_resample = add_suffix(img.absolutepath, "_{}".format(orientation.lower()))
img.save(path=name_resample, mutable=True)
return name_resample
else:
if orientation != 'RPI':
fname = add_suffix(fname, "_RPI")
im_in = change_orientation(im_in, orientation).save(fname)
printv('Image resolution already ' + str(npx) + 'x' + str(npy) + 'xpz')
return fname
def reorient_data(self):
for f in self.fname_metric_lst:
os.rename(
self.fname_metric_lst[f],
add_suffix("".join(extract_fname(self.param.fname_im)[1:]),
'_2reorient'))
im = Image(add_suffix("".join(extract_fname(self.param.fname_im)[1:]), '_2reorient')) \
.change_orientation(self.orientation_im) \
.save(self.fname_metric_lst[f])
def visualize_warp(im_warp: Image, im_grid: Image = None, step=3, rm_tmp=True):
fname_warp = im_warp.absolutepath
if im_grid:
fname_grid = im_grid.absolutepath
else:
tmp_dir = tmp_create()
nx, ny, nz = im_warp.data.shape[0:3]
curdir = os.getcwd()
os.chdir(tmp_dir)
sq = np.zeros((step, step))
sq[step - 1] = 1
sq[:, step - 1] = 1
dat = np.zeros((nx, ny, nz))
for i in range(0, dat.shape[0], step):
for j in range(0, dat.shape[1], step):
for k in range(dat.shape[2]):
if dat[i:i + step, j:j + step, k].shape == (step, step):
dat[i:i + step, j:j + step, k] = sq
im_grid = Image(param=dat)
grid_hdr = im_warp.hdr
im_grid.hdr = grid_hdr
fname_grid = 'grid_' + str(step) + '.nii.gz'
im_grid.save(fname_grid)
fname_grid_resample = add_suffix(fname_grid, '_resample')
sct_resample.main(argv=['-i', fname_grid, '-f', '3x3x1', '-x', 'nn', '-o', fname_grid_resample])
fname_grid = os.path.join(tmp_dir, fname_grid_resample)
os.chdir(curdir)
path_warp, file_warp, ext_warp = extract_fname(fname_warp)
grid_warped = os.path.join(path_warp, extract_fname(fname_grid)[1] + '_' + file_warp + ext_warp)
sct_apply_transfo.main(argv=['-i', fname_grid, '-d', fname_grid, '-w', fname_warp, '-o', grid_warped])
if rm_tmp:
rmtree(tmp_dir)
def multicomponent_merge(fname_list):
from numpy import zeros
# WARNING: output multicomponent is not optimal yet, some issues may be related to the use of this function
im_0 = Image(fname_list[0])
new_shape = list(im_0.data.shape)
if len(new_shape) == 3:
new_shape.append(1)
new_shape.append(len(fname_list))
new_shape = tuple(new_shape)
data_out = zeros(new_shape)
for i, fname in enumerate(fname_list):
im = Image(fname)
dat = im.data
if len(dat.shape) == 2:
data_out[:, :, 0, 0, i] = dat.astype('float32')
elif len(dat.shape) == 3:
data_out[:, :, :, 0, i] = dat.astype('float32')
elif len(dat.shape) == 4:
data_out[:, :, :, :, i] = dat.astype('float32')
del im
del dat
im_out = im_0.copy()
im_out.data = data_out.astype('float32')
im_out.hdr.set_intent('vector', (), '')
im_out.absolutepath = add_suffix(im_out.absolutepath, '_multicomponent')
return im_out
def label_discs(fname_seg, list_disc_z, list_disc_value, verbose=1):
"""
Create file with single voxel label in the middle of the spinal cord for each disc.
:param fname_seg: fname of the segmentation, no orientation expected
:param list_disc_z: list of z that correspond to a disc
:param list_disc_value: list of associated disc values
:param verbose:
:return:
"""
seg = Image(fname_seg)
init_orientation = seg.orientation
seg.change_orientation("RPI")
disc_data = np.zeros_like(seg.data)
nx, ny, nz = seg.data.shape
for i in range(len(list_disc_z)):
if list_disc_z[i] < nz:
try:
slices = seg.data[:, :, list_disc_z[i]]
cx, cy = [int(x) for x in np.round(center_of_mass(slices)).tolist()]
except EmptyArrayError:
logger.warning("During disc labeling, center of mass calculation failed due to discontinuities in "
"segmented spinal cord; please check the quality of your segmentation. Using "
"interpolated centerline as a fallback.")
interpolated_centerline, _, _, _ = get_centerline(seg)
slices = interpolated_centerline.data[:, :, list_disc_z[i]]
cx, cy = [int(x) for x in np.round(center_of_mass(slices)).tolist()]
# Disc value are offset by one due to legacy code
disc_data[cx, cy, list_disc_z[i]] = list_disc_value[i] + 1
seg.data = disc_data
seg.change_orientation(init_orientation).save(add_suffix(fname_seg, '_labeled_disc'))
def main(argv=None):
"""
Main function
:param fname_anat:
:param fname_centerline:
:param verbose:
:return:
"""
parser = get_parser()
arguments = parser.parse_args(argv)
verbose = arguments.v
set_global_loglevel(verbose=verbose)
fname_anat = arguments.i
fname_centerline = arguments.s
# load input images
im_anat = Image(fname_anat)
im_centerline = Image(fname_centerline)
# flatten sagittal
im_anat_flattened = flatten_sagittal(im_anat, im_centerline, verbose)
# save output
fname_out = add_suffix(fname_anat, '_flatten')
im_anat_flattened.save(fname_out)
display_viewer_syntax([fname_anat, fname_out])
def apply_transfo(im_src, im_dest, warp, interp='spline', rm_tmp=True):
# create tmp dir and go in it
tmp_dir = tmp_create()
# copy warping field to tmp dir
copy(warp, tmp_dir)
warp = ''.join(extract_fname(warp)[1:])
# go to tmp dir
curdir = os.getcwd()
os.chdir(tmp_dir)
# save image and seg
fname_src = 'src.nii.gz'
im_src.save(fname_src)
fname_dest = 'dest.nii.gz'
im_dest.save(fname_dest)
# apply warping field
fname_src_reg = add_suffix(fname_src, '_reg')
sct_apply_transfo.main(
argv=['-i', fname_src, '-d', fname_dest, '-w', warp, '-x', interp])
im_src_reg = Image(fname_src_reg)
# get out of tmp dir
os.chdir(curdir)
if rm_tmp:
# remove tmp dir
rmtree(tmp_dir)
# return res image
return im_src_reg
def func_rescale_header(fname_data, rescale_factor, verbose=0):
"""
Rescale the voxel dimension by modifying the NIFTI header qform. Write the output file in a temp folder.
:param fname_data:
:param rescale_factor:
:return: fname_data_rescaled
"""
import nibabel as nib
img = nib.load(fname_data)
# get qform
qform = img.header.get_qform()
# multiply by scaling factor
qform[0:3, 0:3] *= rescale_factor
# generate a new nifti file
header_rescaled = img.header.copy()
header_rescaled.set_qform(qform)
# the data are the same-- only the header changes
img_rescaled = nib.nifti1.Nifti1Image(img.get_data(),
None,
header=header_rescaled)
path_tmp = tmp_create(basename="propseg")
fname_data_rescaled = os.path.join(
path_tmp, os.path.basename(add_suffix(fname_data, "_rescaled")))
nib.save(img_rescaled, fname_data_rescaled)
return fname_data_rescaled
def label_discs(fname_seg, list_disc_z, list_disc_value, verbose=1):
"""
Create file with single voxel label in the middle of the spinal cord for each disc.
:param fname_seg: fname of the segmentation, no orientation expected
:param list_disc_z: list of z that correspond to a disc
:param list_disc_value: list of associated disc values
:param verbose:
:return:
"""
seg = Image(fname_seg)
init_orientation = seg.orientation
seg.change_orientation("RPI")
disc_data = np.zeros_like(seg.data)
nx, ny, nz = seg.data.shape
for i in range(len(list_disc_z)):
if list_disc_z[i] < nz:
slices = seg.data[:, :, list_disc_z[i]]
cx, cy = [
int(x) for x in np.round(center_of_mass(slices)).tolist()
]
# Disc value are offset by one due to legacy code
disc_data[cx, cy, list_disc_z[i]] = list_disc_value[i] + 1
seg.data = disc_data
seg.change_orientation(init_orientation).save(
add_suffix(fname_seg, '_labeled_disc'))
def register_data(im_src, im_dest, param_reg, path_copy_warp=None, rm_tmp=True):
'''
Parameters
----------
im_src: class Image: source image
im_dest: class Image: destination image
param_reg: str: registration parameter
path_copy_warp: path: path to copy the warping fields
Returns: im_src_reg: class Image: source image registered on destination image
-------
'''
# im_src and im_dest are already preprocessed (in theory: im_dest = mean_image)
# binarize images to get seg
im_src_seg = binarize(im_src, thr_min=1, thr_max=1)
im_dest_seg = binarize(im_dest)
# create tmp dir and go in it
tmp_dir = tmp_create()
curdir = os.getcwd()
os.chdir(tmp_dir)
# save image and seg
fname_src = 'src.nii.gz'
im_src.save(fname_src)
fname_src_seg = 'src_seg.nii.gz'
im_src_seg.save(fname_src_seg)
fname_dest = 'dest.nii.gz'
im_dest.save(fname_dest)
fname_dest_seg = 'dest_seg.nii.gz'
im_dest_seg.save(fname_dest_seg)
# do registration using param_reg
sct_register_multimodal.main(args=['-i', fname_src,
'-d', fname_dest,
'-iseg', fname_src_seg,
'-dseg', fname_dest_seg,
'-param', param_reg])
# get registration result
fname_src_reg = add_suffix(fname_src, '_reg')
im_src_reg = Image(fname_src_reg)
# get out of tmp dir
os.chdir(curdir)
# copy warping fields
if path_copy_warp is not None and os.path.isdir(os.path.abspath(path_copy_warp)):
path_copy_warp = os.path.abspath(path_copy_warp)
file_src = extract_fname(fname_src)[1]
file_dest = extract_fname(fname_dest)[1]
fname_src2dest = 'warp_' + file_src + '2' + file_dest + '.nii.gz'
fname_dest2src = 'warp_' + file_dest + '2' + file_src + '.nii.gz'
copy(os.path.join(tmp_dir, fname_src2dest), path_copy_warp)
copy(os.path.join(tmp_dir, fname_dest2src), path_copy_warp)
if rm_tmp:
# remove tmp dir
rmtree(tmp_dir)
# return res image
return im_src_reg, fname_src2dest, fname_dest2src
def pad_image(im,
pad_x_i=0,
pad_x_f=0,
pad_y_i=0,
pad_y_f=0,
pad_z_i=0,
pad_z_f=0):
nx, ny, nz, nt, px, py, pz, pt = im.dim
pad_x_i, pad_x_f, pad_y_i, pad_y_f, pad_z_i, pad_z_f = int(pad_x_i), int(
pad_x_f), int(pad_y_i), int(pad_y_f), int(pad_z_i), int(pad_z_f)
if len(im.data.shape) == 2:
new_shape = list(im.data.shape)
new_shape.append(1)
im.data = im.data.reshape(new_shape)
# initialize padded_data, with same type as im.data
padded_data = np.zeros((nx + pad_x_i + pad_x_f, ny + pad_y_i + pad_y_f,
nz + pad_z_i + pad_z_f),
dtype=im.data.dtype)
if pad_x_f == 0:
pad_x_f = None
elif pad_x_f > 0:
pad_x_f *= -1
if pad_y_f == 0:
pad_y_f = None
elif pad_y_f > 0:
pad_y_f *= -1
if pad_z_f == 0:
pad_z_f = None
elif pad_z_f > 0:
pad_z_f *= -1
padded_data[pad_x_i:pad_x_f, pad_y_i:pad_y_f, pad_z_i:pad_z_f] = im.data
im_out = im.copy()
# TODO: Do not copy the Image(), because the dim field and hdr.get_data_shape() will not be updated properly.
# better to just create a new Image() from scratch.
im_out.data = padded_data # done after the call of the function
im_out.absolutepath = add_suffix(im_out.absolutepath, "_pad")
# adapt the origin in the sform and qform matrix
new_origin = np.dot(im_out.hdr.get_qform(),
[-pad_x_i, -pad_y_i, -pad_z_i, 1])
im_out.hdr.structarr['qoffset_x'] = new_origin[0]
im_out.hdr.structarr['qoffset_y'] = new_origin[1]
im_out.hdr.structarr['qoffset_z'] = new_origin[2]
im_out.hdr.structarr['srow_x'][-1] = new_origin[0]
im_out.hdr.structarr['srow_y'][-1] = new_origin[1]
im_out.hdr.structarr['srow_z'][-1] = new_origin[2]
return im_out
def __init__(self, im, v=1):
printv('Thinning ... ', v, 'normal')
self.image = im
self.image.data = bin_data(self.image.data)
self.dim_im = len(self.image.data.shape)
if self.dim_im == 2:
self.thinned_image = empty_like(self.image)
self.thinned_image.data = self.zhang_suen(self.image.data)
self.thinned_image.absolutepath = add_suffix(self.image.absolutepath, "_thinned")
elif self.dim_im == 3:
if not self.image.orientation == 'IRP':
printv('-- changing orientation ...')
self.image.change_orientation('IRP')
thinned_data = np.asarray([self.zhang_suen(im_slice) for im_slice in self.image.data])
self.thinned_image = empty_like(self.image)
self.thinned_image.data = thinned_data
self.thinned_image.absolutepath = add_suffix(self.image.absolutepath, "_thinned")
def compute_texture(self):
offset = int(self.param_glcm.distance)
printv('\nCompute texture metrics...', self.param.verbose, 'normal')
# open image and re-orient it to RPI if needed
im_tmp = Image(self.param.fname_im)
if self.orientation_im != self.orientation_extraction:
im_tmp.change_orientation(self.orientation_extraction)
dct_metric = {}
for m in self.metric_lst:
im_2save = zeros_like(im_tmp, dtype='float64')
dct_metric[m] = im_2save
# dct_metric[m] = Image(self.fname_metric_lst[m])
with sct_progress_bar() as pbar:
for im_z, seg_z, zz in zip(self.dct_im_seg['im'], self.dct_im_seg['seg'], range(len(self.dct_im_seg['im']))):
for xx in range(im_z.shape[0]):
for yy in range(im_z.shape[1]):
if not seg_z[xx, yy]:
continue
if xx < offset or yy < offset:
continue
if xx > (im_z.shape[0] - offset - 1) or yy > (im_z.shape[1] - offset - 1):
continue # to check if the whole glcm_window is in the axial_slice
if False in np.unique(seg_z[xx - offset: xx + offset + 1, yy - offset: yy + offset + 1]):
continue # to check if the whole glcm_window is in the mask of the axial_slice
glcm_window = im_z[xx - offset: xx + offset + 1, yy - offset: yy + offset + 1]
glcm_window = glcm_window.astype(np.uint8)
dct_glcm = {}
for a in self.param_glcm.angle.split(','): # compute the GLCM for self.param_glcm.distance and for each self.param_glcm.angle
dct_glcm[a] = greycomatrix(glcm_window,
[self.param_glcm.distance], [np.radians(int(a))],
symmetric=self.param_glcm.symmetric,
normed=self.param_glcm.normed)
for m in self.metric_lst: # compute the GLCM property (m.split('_')[0]) of the voxel xx,yy,zz
dct_metric[m].data[xx, yy, zz] = greycoprops(dct_glcm[m.split('_')[2]], m.split('_')[0])[0][0]
pbar.set_postfix(pos="{}/{}".format(zz, len(self.dct_im_seg["im"])))
pbar.update(1)
for m in self.metric_lst:
fname_out = add_suffix("".join(extract_fname(self.param.fname_im)[1:]), '_' + m)
dct_metric[m].save(fname_out)
self.fname_metric_lst[m] = fname_out
def visualize_warp(fname_warp, fname_grid=None, step=3, rm_tmp=True):
if fname_grid is None:
from numpy import zeros
tmp_dir = tmp_create()
im_warp = Image(fname_warp)
status, out = run_proc(['fslhd', fname_warp])
curdir = os.getcwd()
os.chdir(tmp_dir)
dim1 = 'dim1 '
dim2 = 'dim2 '
dim3 = 'dim3 '
nx = int(
out[out.find(dim1):][len(dim1):out[out.find(dim1):].find('\n')])
ny = int(
out[out.find(dim2):][len(dim2):out[out.find(dim2):].find('\n')])
nz = int(
out[out.find(dim3):][len(dim3):out[out.find(dim3):].find('\n')])
sq = zeros((step, step))
sq[step - 1] = 1
sq[:, step - 1] = 1
dat = zeros((nx, ny, nz))
for i in range(0, dat.shape[0], step):
for j in range(0, dat.shape[1], step):
for k in range(dat.shape[2]):
if dat[i:i + step, j:j + step, k].shape == (step, step):
dat[i:i + step, j:j + step, k] = sq
fname_grid = 'grid_' + str(step) + '.nii.gz'
im_grid = Image(param=dat)
grid_hdr = im_warp.hdr
im_grid.hdr = grid_hdr
im_grid.absolutepath = fname_grid
im_grid.save()
fname_grid_resample = add_suffix(fname_grid, '_resample')
run_proc([
'sct_resample', '-i', fname_grid, '-f', '3x3x1', '-x', 'nn', '-o',
fname_grid_resample
])
fname_grid = os.path.join(tmp_dir, fname_grid_resample)
os.chdir(curdir)
path_warp, file_warp, ext_warp = extract_fname(fname_warp)
grid_warped = os.path.join(
path_warp,
extract_fname(fname_grid)[1] + '_' + file_warp + ext_warp)
run_proc([
'sct_apply_transfo', '-i', fname_grid, '-d', fname_grid, '-w',
fname_warp, '-o', grid_warped
])
if rm_tmp:
rmtree(tmp_dir)
def label_discs(fname_seg_labeled, verbose=1):
"""
Label discs from labeled_segmentation. The convention is C2/C3-->3, C3/C4-->4, etc.
:param fname_seg_labeld: fname of the labeled segmentation
:param verbose:
:return:
"""
# open labeled segmentation
im_seg_labeled = Image(fname_seg_labeled)
orientation_native = im_seg_labeled.orientation
im_seg_labeled.change_orientation("RPI")
nx, ny, nz = im_seg_labeled.dim[0], im_seg_labeled.dim[
1], im_seg_labeled.dim[2]
data_disc = np.zeros([nx, ny, nz])
vertebral_level_previous = np.max(
im_seg_labeled.data) # initialize with the max label value
# loop across z in the superior direction (i.e. starts with the bottom slice), and each time the i/i+1 interface
# between two levels is found, create a label at the center of the cord with the value corresponding to the
# vertebral level below the point. E.g., at interface C3/C4, the value would be 4.
for iz in range(nz):
# get 2d slice
slice = im_seg_labeled.data[:, :, iz]
# check if at least one voxel is non-zero
if np.any(slice):
slice_one = np.copy(slice)
# set all non-zero values to 1 (to compute center of mass)
# Note: the reason we do this is because if one slice includes part of vertebral level i and i+1, the
# center of mass will be shifted towards the i+1 level.We don't want that here (i.e. the goal is to be at
# the center of the cord)
slice_one[slice.nonzero()] = 1
# compute center of mass
cx, cy = [
int(x) for x in np.round(center_of_mass(slice_one)).tolist()
]
# retrieve vertebral level
vertebral_level = slice[cx, cy]
# if smaller than previous level, then labeled as a disc
if vertebral_level < vertebral_level_previous:
# label disc
data_disc[cx, cy, iz] = vertebral_level + 1
# update variable
vertebral_level_previous = vertebral_level
# save disc labeled file
im_seg_labeled.data = data_disc
im_seg_labeled.change_orientation(orientation_native).save(
add_suffix(fname_seg_labeled, '_disc'))
def main(argv=None):
parser = get_parser()
arguments = parser.parse_args(argv if argv else ['--help'])
verbose = arguments.v
set_global_loglevel(verbose=verbose)
input_filename = arguments.i
if arguments.o is not None:
output_filename = arguments.o
else:
output_filename = add_suffix(input_filename, '_gmseg')
use_tta = arguments.t
model_name = arguments.m
threshold = arguments.thr
if threshold > 1.0 or threshold < 0.0:
raise RuntimeError("Threshold should be between 0.0 and 1.0.")
# Threshold zero means no thresholding
if threshold == 0.0:
threshold = None
from spinalcordtoolbox.deepseg_gm import deepseg_gm
deepseg_gm.check_backend()
out_fname = deepseg_gm.segment_file(input_filename,
output_filename, model_name, threshold,
int(verbose), use_tta)
path_qc = arguments.qc
qc_dataset = arguments.qc_dataset
qc_subject = arguments.qc_subject
if path_qc is not None:
generate_qc(fname_in1=input_filename,
fname_seg=out_fname,
args=sys.argv[1:],
path_qc=os.path.abspath(path_qc),
dataset=qc_dataset,
subject=qc_subject,
process='sct_deepseg_gm')
display_viewer_syntax([input_filename, format(out_fname)],
colormaps=['gray', 'red'],
opacities=['1', '0.7'],
verbose=verbose)
def resample_file(fname_data,
fname_out,
new_size,
new_size_type,
interpolation,
verbose,
fname_ref=None):
"""This function will resample the specified input
image file to the target size.
Can deal with 2d, 3d or 4d image objects.
:param fname_data: The input image filename.
:param fname_out: The output image filename.
:param new_size: The target size, i.e. 0.25x0.25
:param new_size_type: Unit of resample (mm, vox, factor)
:param interpolation: The interpolation type
:param verbose: verbosity level
:param fname_ref: Reference image to resample input image to
"""
# Load data
logger.info('load data...')
nii = nib.load(fname_data)
if fname_ref is not None:
nii_ref = nib.load(fname_ref)
else:
nii_ref = None
nii_r = resample_nib(nii,
new_size.split('x'),
new_size_type,
image_dest=nii_ref,
interpolation=interpolation)
# build output file name
if fname_out == '':
fname_out = add_suffix(fname_data, '_r')
else:
fname_out = fname_out
# save data
nib.save(nii_r, fname_out)
# to view results
display_viewer_syntax([fname_out], verbose=verbose)
return nii_r
def main(argv=None):
parser = get_parser()
arguments = parser.parse_args(argv)
verbose = arguments.v
set_loglevel(verbose=verbose)
param = Param()
fname_src = arguments.i
if arguments.o is not None:
fname_dst = arguments.o
else:
fname_dst = add_suffix(fname_src, "_tsnr")
# call main function
tsnr = Tsnr(param=param, fmri=fname_src, out=fname_dst)
tsnr.compute()
def test_sct_dmri_denoise_patch2self(dmri_concat, tmp_path, model, radius):
fname_dmri, fname_bvals, _ = dmri_concat # dMRI file with >=10 volumes
fname_out = str(tmp_path / "dmri_denoised.nii.gz")
sct_dmri_denoise_patch2self.main(argv=[
'-i', fname_dmri, '-b', fname_bvals, '-model', model, '-radius',
radius, '-o', fname_out, '-v', '2'
])
snr = []
for fname in fname_dmri, fname_out:
sct_compute_snr.main(argv=['-i', fname, '-method', 'mult'])
img_snr = Image(add_suffix(fname, "_SNR-mult"))
snr.append(img_snr.data.mean())
# Mean SNR should increase. Not a thorough test; I just wanted to verify
# that it even works at all, as the actual underlying method is tested
# extensively by dipy upstream.
assert snr[1] > snr[0]
def main(args=None):
parser = get_parser()
param = Param()
arguments = parser.parse_args(args=None if sys.argv[1:] else ['--help'])
fname_src = arguments.i
if arguments.o is not None:
fname_dst = arguments.o
else:
fname_dst = add_suffix(fname_src, "_tsnr")
verbose = int(arguments.v)
init_sct(log_level=verbose, update=True) # Update log level
# call main function
tsnr = Tsnr(param=param, fmri=fname_src, out=fname_dst)
tsnr.compute()
def main(fname_anat, fname_centerline, verbose):
"""
Main function
:param fname_anat:
:param fname_centerline:
:param verbose:
:return:
"""
# load input images
im_anat = Image(fname_anat)
im_centerline = Image(fname_centerline)
# flatten sagittal
im_anat_flattened = flatten_sagittal(im_anat, im_centerline, verbose)
# save output
fname_out = add_suffix(fname_anat, '_flatten')
im_anat_flattened.save(fname_out)
display_viewer_syntax([fname_anat, fname_out])
def main(args=None):
"""
Main function
:param args:
:return:
"""
# get parser args
if args is None:
args = None if sys.argv[1:] else ['--help']
parser = get_parser()
arguments = parser.parse_args(args=args)
# initialize ImageCropper
cropper = ImageCropper(Image(arguments.i))
cropper.verbose = arguments.v
init_sct(log_level=cropper.verbose, update=True) # Update log level
# Switch across cropping methods
if arguments.g:
cropper.get_bbox_from_gui()
elif arguments.m:
cropper.get_bbox_from_mask(Image(arguments.m))
elif arguments.ref:
cropper.get_bbox_from_ref(Image(arguments.ref))
else:
cropper.get_bbox_from_minmax(
BoundingBox(arguments.xmin, arguments.xmax, arguments.ymin,
arguments.ymax, arguments.zmin, arguments.zmax))
# Crop image
img_crop = cropper.crop(background=arguments.b)
# Write cropped image to file
if arguments.o is None:
fname_out = add_suffix(arguments.i, '_crop')
else:
fname_out = arguments.o
img_crop.save(fname_out)
display_viewer_syntax([arguments.i, fname_out])
def main(argv=None):
"""
Main function
:param argv:
:return:
"""
parser = get_parser()
arguments = parser.parse_args(argv if argv else ['--help'])
verbose = arguments.v
set_global_loglevel(verbose=verbose)
# initialize ImageCropper
cropper = ImageCropper(Image(arguments.i))
cropper.verbose = verbose
# Switch across cropping methods
if arguments.g:
cropper.get_bbox_from_gui()
elif arguments.m:
cropper.get_bbox_from_mask(Image(arguments.m))
elif arguments.ref:
cropper.get_bbox_from_ref(Image(arguments.ref))
else:
cropper.get_bbox_from_minmax(
BoundingBox(arguments.xmin, arguments.xmax,
arguments.ymin, arguments.ymax,
arguments.zmin, arguments.zmax)
)
# Crop image
img_crop = cropper.crop(background=arguments.b)
# Write cropped image to file
if arguments.o is None:
fname_out = add_suffix(arguments.i, '_crop')
else:
fname_out = arguments.o
img_crop.save(fname_out)
display_viewer_syntax([arguments.i, fname_out])
def label_segmentation(fname_seg, list_disc_z, list_disc_value, verbose=1):
"""
Label segmentation image
:param fname_seg: fname of the segmentation, no orientation expected
:param list_disc_z: list of z that correspond to a disc
:param list_disc_value: list of associated disc values
:param verbose:
:return:
"""
# open segmentation
seg = Image(fname_seg)
init_orientation = seg.orientation
seg.change_orientation("RPI")
dim = seg.dim
ny = dim[1]
nz = dim[2]
# loop across z
for iz in range(nz):
# get index of the disc right above iz
try:
ind_above_iz = max(
[i for i in range(len(list_disc_z)) if list_disc_z[i] > iz])
except ValueError:
# if ind_above_iz is empty, attribute value 0
vertebral_level = 0
else:
# assign vertebral level (add one because iz is BELOW the disk)
vertebral_level = list_disc_value[ind_above_iz] + 1
# get voxels in mask
ind_nonzero = np.nonzero(seg.data[:, :, iz])
seg.data[ind_nonzero[0], ind_nonzero[1], iz] = vertebral_level
# write file
seg.change_orientation(init_orientation).save(
add_suffix(fname_seg, '_labeled'))
def multicomponent_split(im):
"""
Convert composite image (e.g., ITK warping field, 5dim) into several 3d volumes.
Replaces "c3d -mcs warp_comp.nii -oo warp_vecx.nii warp_vecy.nii warp_vecz.nii"
:param im:
:return:
"""
data = im.data
assert len(data.shape) == 5
data_out = []
for i in range(data.shape[-1]):
dat_out = data[:, :, :, :, i]
'''
while dat_out.shape[-1] == 1:
dat_out = reshape(dat_out, dat_out.shape[:-1])
'''
data_out.append(dat_out) # .astype('float32'))
im_out = [im.copy() for j in range(len(data_out))]
for i, im in enumerate(im_out):
im.data = data_out[i]
im.hdr.set_intent('vector', (), '')
im.absolutepath = add_suffix(im.absolutepath, '_{}'.format(i))
return im_out
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(argv=[
'-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_data_splitZ_splitT_moco = [
Image(fname) for fname in file_data_splitZ_splitT_moco
]
im_out = concat_data(im_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_data_splitZ_moco = [Image(fname) for fname in file_data_splitZ_moco]
im_out = concat_data(im_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 main(argv=None):
parser = get_parser()
arguments = parser.parse_args(argv if argv else ['--help'])
verbose = arguments.v
set_global_loglevel(verbose=verbose)
fname_input1 = arguments.i
fname_input2 = arguments.d
tmp_dir = tmp_create() # create tmp directory
tmp_dir = os.path.abspath(tmp_dir)
# copy input files to tmp directory
# for fname in [fname_input1, fname_input2]:
copy(fname_input1, tmp_dir)
copy(fname_input2, tmp_dir)
fname_input1 = ''.join(extract_fname(fname_input1)[1:])
fname_input2 = ''.join(extract_fname(fname_input2)[1:])
curdir = os.getcwd()
os.chdir(tmp_dir) # go to tmp directory
if arguments.bin is not None:
fname_input1_bin = add_suffix(fname_input1, '_bin')
run_proc(['sct_maths', '-i', fname_input1, '-bin', '0', '-o', fname_input1_bin])
fname_input1 = fname_input1_bin
fname_input2_bin = add_suffix(fname_input2, '_bin')
run_proc(['sct_maths', '-i', fname_input2, '-bin', '0', '-o', fname_input2_bin])
fname_input2 = fname_input2_bin
# copy header of im_1 to im_2
from spinalcordtoolbox.image import Image
im_1 = Image(fname_input1)
im_2 = Image(fname_input2)
im_2.header = im_1.header
im_2.save()
cmd = ['isct_dice_coefficient', fname_input1, fname_input2]
if vars(arguments)["2d_slices"] is not None:
cmd += ['-2d-slices', str(vars(arguments)["2d_slices"])]
if arguments.b is not None:
bounding_box = arguments.b
cmd += ['-b'] + bounding_box
if arguments.bmax is not None and arguments.bmax == 1:
cmd += ['-bmax']
if arguments.bzmax is not None and arguments.bzmax == 1:
cmd += ['-bzmax']
if arguments.o is not None:
path_output, fname_output, ext = extract_fname(arguments.o)
cmd += ['-o', fname_output + ext]
rm_tmp = bool(arguments.r)
# # Computation of Dice coefficient using Python implementation.
# # commented for now as it does not cover all the feature of isct_dice_coefficient
# #from spinalcordtoolbox.image import Image, compute_dice
# #dice = compute_dice(Image(fname_input1), Image(fname_input2), mode='3d', zboundaries=False)
# #printv('Dice (python-based) = ' + str(dice), verbose)
status, output = run_proc(cmd, verbose, is_sct_binary=True)
os.chdir(curdir) # go back to original directory
# copy output file into original directory
if arguments.o is not None:
copy(os.path.join(tmp_dir, fname_output + ext), os.path.join(path_output, fname_output + ext))
# remove tmp_dir
if rm_tmp:
rmtree(tmp_dir)
printv(output, verbose)
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'
fname_dw_list = []
for iGroup in range(nb_groups):
fname_dw_list.append(list_file_group[iGroup])
im_dw_list = [Image(fname) for fname in fname_dw_list]
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(
argv=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 = [Image(fname) for fname in files_warp]
# im_warp_concat = concat_data(im_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_X = [Image(fname) for fname in files_warp_X]
im_warp_concat = concat_data(im_warp_X, dim=3)
im_warp_concat.save(file_moco_params_x)
im_warp_Y = [Image(fname) for fname in files_warp_Y]
im_warp_concat = concat_data(im_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')
