def test_compute_brain_mask():
img, _ = data_gen.generate_mni_space_img(res=8, random_state=0)
brain_mask = compute_brain_mask(img, threshold=.2)
gm_mask = compute_brain_mask(img, threshold=.2, mask_type="gm")
wm_mask = compute_brain_mask(img, threshold=.2, mask_type="wm")
brain_data, gm_data, wm_data = map(get_data,
(brain_mask, gm_mask, wm_mask))
# Check that whole-brain mask is non-empty
assert (brain_data != 0).any()
for subset in gm_data, wm_data:
# Test that gm and wm masks are included in the whole-brain mask
assert (np.logical_and(brain_data,
subset) == subset.astype(bool)).all()
# Test that gm and wm masks are non-empty
assert (subset != 0).any()
# Test that gm and wm masks have empty intersection
assert (np.logical_and(gm_data, wm_data) == 0).all()
# Check that we get a useful warning for empty masks
with pytest.warns(masking.MaskWarning):
compute_brain_mask(img, threshold=1)
# Check that masks obtained from same FOV are the same
img1, _ = data_gen.generate_mni_space_img(res=8, random_state=1)
mask_img1 = compute_brain_mask(img1, verbose=1, threshold=.2)
assert (brain_data == get_data(mask_img1)).all()
# Check that error is raised if mask type is unknown
with pytest.raises(ValueError, match='Unknown mask type foo.'):
compute_brain_mask(img, verbose=1, mask_type='foo')
def test_compute_brain_mask():
image = Nifti1Image(np.ones((9, 9, 9)), np.eye(4))
mask = compute_brain_mask(image, threshold=-1)
mask1 = np.zeros((9, 9, 9))
mask1[2:-2, 2:-2, 2:-2] = 1
np.testing.assert_array_equal(mask1, get_data(mask))
# Check that we get a useful warning for empty masks
with pytest.warns(masking.MaskWarning):
compute_brain_mask(image, threshold=1)
# Check that masks obtained from same FOV are the same
img1 = Nifti1Image(np.full((9, 9, 9), np.random.rand()), np.eye(4))
img2 = Nifti1Image(np.full((9, 9, 9), np.random.rand()), np.eye(4))
mask_img1 = compute_brain_mask(img1)
mask_img2 = compute_brain_mask(img2)
np.testing.assert_array_equal(get_data(mask_img1),
get_data(mask_img2))
def t1_pipeline(do_normalise_before=False,
do_segment=True,
do_normalise_after=False,
do_plot=True,
keep_tmp=True,
sub_name='sub-11',
sess_num='ses-17',
root_path='/neurospin/ibc'):
"""
Preprocess qMRI t1 images and then run estimation to generate t1-maps,
more details in scripts/qmri_README.md,
only one of do_normalise_before and do_normalise_after should be True,
both can be False
"""
DATA_LOC = join(root_path, 'sourcedata', sub_name, sess_num)
SAVE_TO = join(root_path, 'derivatives', sub_name, sess_num)
if do_normalise_before or do_normalise_after:
space = "MNI152"
else:
space = "individual"
if not exists(SAVE_TO):
makedirs(SAVE_TO)
start_time = time.time()
# data files
data_dir = DATA_LOC
niftis = []
jsons = []
for fi in listdir(join(data_dir, 'anat')):
if fi.split('_')[-1] == 'T1map.nii.gz':
niftis.append(join(data_dir, 'anat', fi))
elif fi.split('_')[-1] == 'T1map.json':
jsons.append(join(data_dir, 'anat', fi))
elif fi.split('_')[-1] == 'B1map.nii.gz':
b1_map_nifti = join(data_dir, 'anat', fi)
elif fi.split('_')[-1] == 'B1map.json':
b1_map_json = join(data_dir, 'anat', fi)
else:
continue
niftis.sort()
jsons.sort()
# preprocessing directory setup
time_elapsed = time.time() - start_time
print('[INFO, t={:.2f}s] copying necessary files...'.format(time_elapsed))
cwd = SAVE_TO
preproc_dir = join(cwd, 'tmp_t1', 'preproc')
if not exists(preproc_dir):
makedirs(preproc_dir)
# copy data files to tmp_t1 directory
cnt = 0
for nii, json in zip(niftis, jsons):
system('cp {} {}'.format(nii, preproc_dir))
niftis[cnt] = join(preproc_dir, nii.split(sep)[-1])
system('gunzip -df {}'.format(niftis[cnt]))
niftis[cnt] = join(preproc_dir,
nii.split(sep)[-1].split('.')[0] + '.nii')
system('cp {} {}'.format(json, preproc_dir))
jsons[cnt] = join(preproc_dir, json.split(sep)[-1])
cnt += 1
system('cp {} {}'.format(b1_map_nifti, preproc_dir))
b1_map_nifti = join(preproc_dir, b1_map_nifti.split(sep)[-1])
system('gunzip -df {}'.format(b1_map_nifti))
b1_map_nifti = join(preproc_dir,
b1_map_nifti.split(sep)[-1].split('.')[0] + '.nii')
system('cp {} {}'.format(b1_map_json, preproc_dir))
b1_map_json = join(preproc_dir, b1_map_json.split(sep)[-1])
# preprocessing step: spatial normalization to MNI space
# of T1 maps
if do_normalise_before:
time_elapsed = time.time() - start_time
print('[INFO, t={:.2f}s] segmenting highest flip angle image'.format(
time_elapsed))
image = niftis[-1]
out_info = segment(image, True)
# save normalised segments
segments = [
join(preproc_dir, f"w{out_info[segment].split('/')[-1]}")
for segment in ['gm', 'wm']
]
time_elapsed = time.time() - start_time
print('[INFO, t={:.2f}s] transforming images to MNI space...'.format(
time_elapsed))
normed_niftis = []
cnt = 0
for nii in niftis:
image = nii
if cnt == len(niftis) - 1:
b1_map = b1_map_nifti
out_info = to_MNI(image, data=out_info, func=b1_map)
normed_b1_map = out_info['func'][0]
else:
out_info = to_MNI(image, data=out_info)
normed_niftis.append(out_info['anat'])
time_elapsed = time.time() - start_time
print('[INFO, t={:.2f}s] \t transformed {}'.format(
time_elapsed,
nii.split(sep)[-1]))
cnt = cnt + 1
# preprocessing step: segmenting largest flip angle image
if do_segment:
time_elapsed = time.time() - start_time
if do_normalise_before:
print('[INFO, t={:.2f}s] creating a mask'.format(time_elapsed))
image = normed_niftis[-1]
mni = compute_brain_mask(image)
closed_mni = closing(mni, 12)
union = intersect_masks([mni, closed_mni], threshold=0)
else:
print('[INFO, t={:.2f}s] segmenting highest flip angle image'.
format(time_elapsed))
image = niftis[-1]
out_info = segment(image, True)
segments = [out_info['gm'], out_info['wm']]
time_elapsed = time.time() - start_time
print('[INFO, t={:.2f}s] \t segmented {}'.format(
time_elapsed,
image.split(sep)[-1]))
# preprocessing step: creating a mask
print('[INFO, t={:.2f}s] creating a mask using segments'.format(
time_elapsed))
add = math_img("img1 + img2", img1=segments[0], img2=segments[1])
if sub_name == 'sub-08':
full = compute_epi_mask(add, exclude_zeros=True)
else:
full = compute_epi_mask(add)
insides = compute_background_mask(full, opening=12)
union = intersect_masks([full, insides], threshold=0)
mask_file = join(preproc_dir, 'mask.nii')
union.to_filename(mask_file)
# estimation directory setup
time_elapsed = time.time() - start_time
print('[INFO, t={:.2f}s] starting estimation...'.format(time_elapsed))
recon_dir = join(cwd, 'tmp_t1', 'recon')
if not exists(recon_dir):
makedirs(recon_dir)
jsons_str = ' '.join(jsons)
if do_normalise_before:
niftis_str = ' '.join(normed_niftis)
b1_map_str = normed_b1_map
else:
niftis_str = ' '.join(niftis)
b1_map_str = b1_map_nifti
if do_segment == False:
mask_file = None
# estimation: parameter extraction
system(f"python3 ../scripts/qmri_t1_map_b1_params.py\
-v 1\
-s {sub_name}\
-o {recon_dir}\
-g {jsons_str}\
-b {b1_map_json}")
# estimation: t1 estimation
system(f"python3 ../scripts/qmri_t1_map_b1.py\
-v 1\
-s {sub_name}\
-o {recon_dir}\
-g {niftis_str}\
-b {b1_map_str}\
-r {join(recon_dir,f'{sub_name}_t1_map_b1.json')}\
-d fit\
-m {mask_file}")
recon_map = join(recon_dir, f'{sub_name}_T1map.nii.gz')
# postprocessing: normalization of reconstructed t1 map
if do_normalise_after:
postproc_dir = join(cwd, 'tmp_t1', 'postproc')
if not exists(postproc_dir):
makedirs(postproc_dir)
recon_map = join(recon_dir, f'{sub_name}_T1map.nii.gz')
system('cp {} {}'.format(recon_map, postproc_dir))
time_elapsed = time.time() - start_time
print('[INFO, t={:.2f}s] normalizing reconstructed t1 map...'.format(
time_elapsed))
image = join(postproc_dir, f'{sub_name}_T1map.nii.gz')
system('gunzip -df {}'.format(image))
image = join(postproc_dir, f'{sub_name}_T1map.nii')
out_info = to_MNI(image, segmented=out_info.nipype_results['segment'])
norm_recon_map = out_info['anat']
# doing the plots
if do_plot:
time_elapsed = time.time() - start_time
print('\n[INFO, t={:.2f}s] plotting the map...'.format(time_elapsed))
plot_dir = join(cwd, 'tmp_t1', 'plot')
if not exists(plot_dir):
makedirs(plot_dir)
if do_normalise_after:
t1_img = norm_recon_map
else:
t1_img = join(recon_dir, f'{sub_name}_T1map.nii.gz')
plot_thresholded_qmap(img=t1_img,
thresh="99",
map=f"{sub_name}_T1map_fit",
interactive=True,
coords=(10, 56, 43),
output_folder=plot_dir)
time_elapsed = time.time() - start_time
print('\n[INFO, t={:.2f}s] DONE'.format(time_elapsed))
# move derived files out and delete tmp_t1 directory
final_recon_map = join(SAVE_TO, f'{sub_name}_space-{space}_T1map.nii.gz')
if do_normalise_after:
system('gzip {}'.format(norm_recon_map))
recon_map = norm_recon_map + '.gz'
shutil.move(recon_map, final_recon_map)
if do_plot:
for fi in listdir(plot_dir):
plot_name = join(plot_dir, fi)
final_plot_name = join(
SAVE_TO,
final_recon_map.split(sep)[-1].split('.')[0] + '_' +
fi.split('_')[-1])
shutil.move(plot_name, final_plot_name)
if not keep_tmp:
shutil.rmtree(join(SAVE_TO, 'tmp_t1'))
time_elapsed = time.time() - start_time
print('\n[INFO, t={:.2f}s] created {} \n\n'.format(
time_elapsed, final_recon_map))
def t2_pipeline(do_coreg=True,
do_normalise_before=False,
do_segment=True,
do_normalise_after=False,
do_plot=True,
keep_tmp=True,
sub_name='sub-11',
sess_num='ses-17',
root_path='/neurospin/ibc'):
"""
Preprocess qMRI t2 images and then run estimation to generate t2-maps,
more details in scripts/qmri_README.md,
only one of do_normalise_before and do_normalise_after should be True,
both can be False
"""
DATA_LOC = join(root_path, 'sourcedata', sub_name, sess_num)
SAVE_TO = join(root_path, 'derivatives', sub_name, sess_num)
if do_normalise_before or do_normalise_after:
space = "MNI152"
else:
space = "individual"
if not exists(SAVE_TO):
makedirs(SAVE_TO)
start_time = time.time()
# data files
data_dir = DATA_LOC
niftis = []
jsons = []
for fi in listdir(join(data_dir, 'anat')):
if fi.split('_')[-1] == 'T2map.nii.gz':
niftis.append(join(data_dir, 'anat', fi))
elif fi.split('_')[-1] == 'T2map.json':
jsons.append(join(data_dir, 'anat', fi))
else:
continue
niftis.sort()
jsons.sort()
run_count = 0
for nifti in niftis:
# preprocessing directory setup
time_elapsed = time.time() - start_time
print('[INFO, t={:.2f}s] copying necessary files...'.format(
time_elapsed))
cwd = SAVE_TO
preproc_dir = join(cwd, 'tmp_t2', 'preproc')
if not exists(preproc_dir):
makedirs(preproc_dir)
system('cp {} {}'.format(nifti, preproc_dir))
nifti = join(preproc_dir, nifti.split(sep)[-1])
system('gunzip -df {}'.format(nifti))
nifti = join(preproc_dir, nifti.split(sep)[-1].split('.')[0] + '.nii')
if do_coreg:
# t1 image as an anatomical image
t1_niftis = []
for fi in listdir(join(data_dir, 'anat')):
if fi.split('_')[-1] == 'T1map.nii.gz':
t1_niftis.append(join(data_dir, 'anat', fi))
t1_niftis.sort()
t1_nifti = t1_niftis[-1]
system('cp {} {}'.format(t1_nifti, preproc_dir))
t1_nifti = join(preproc_dir, t1_nifti.split(sep)[-1])
system('gunzip -df {}'.format(t1_nifti))
t1_nifti = join(preproc_dir,
t1_nifti.split(sep)[-1].split('.')[0] + '.nii')
# preprocessing step: spatial normalization to MNI space
# of T1 maps
if do_normalise_before:
image = nifti
if do_coreg:
t1_img = t1_nifti
time_elapsed = time.time() - start_time
print('[INFO, t={:.2f}s] segmenting the t1 image'.format(
time_elapsed))
out_info = segment(t1_img, False)
time_elapsed = time.time() - start_time
print('[INFO, t={:.2f}s] transforming images to MNI space...'.
format(time_elapsed))
out_info = to_MNI(image=t1_img, data=out_info, func=image)
normed_t1_img = out_info['anat']
normed_nifti = out_info['func'][0]
else:
time_elapsed = time.time() - start_time
print('[INFO, t={:.2f}s] segmenting the t2 image'.format(
time_elapsed))
out_info = segment(image, False)
time_elapsed = time.time() - start_time
print('[INFO, t={:.2f}s] transforming images to MNI space...'.
format(time_elapsed))
out_info = to_MNI(image, data=out_info)
normed_nifti = out_info['anat']
time_elapsed = time.time() - start_time
print('[INFO, t={:.2f}s] \t transformed {}'.format(
time_elapsed,
nifti.split(sep)[-1]))
# preprocessing step: transform t1 image to t2 space
if do_coreg:
time_elapsed = time.time() - start_time
print('[INFO, t={:.2f}s] transforming t1 image to t2 space...'.
format(time_elapsed))
if do_normalise_before:
t2_img = normed_nifti
t1_img = normed_t1_img
else:
t2_img = nifti
t1_img = t1_nifti
mean_t2 = mean_img(t2_img)
mean_t2_img = join(preproc_dir,
'mean_{}'.format(t2_img.split(sep)[-1]))
mean_t2.to_filename(mean_t2_img)
out_info = to_T2space(t2_img=mean_t2_img,
t1_img=t1_img,
output_dir=preproc_dir)
print('[INFO, t={:.2f}s] \t transformed {} to {} space'.format(
time_elapsed,
t1_img.split(sep)[-1],
mean_t2_img.split(sep)[-1]))
# preprocessing step: segmenting largest flip angle image
if do_segment:
if do_normalise_before:
time_elapsed = time.time() - start_time
print(
'[INFO, t={:.2f}s] creating a mask'.format(time_elapsed))
if do_coreg:
image = normed_t1_img
else:
image = normed_nifti
mni = compute_brain_mask(image)
closed_mni = closing(mni, 12)
union = intersect_masks([mni, closed_mni], threshold=0)
else:
if do_coreg:
image = t1_img
else:
image = nifti
time_elapsed = time.time() - start_time
print(
'[INFO, t={:.2f}s] segmenting the image for creating a mask'
.format(time_elapsed))
out_info = segment(image, False)
segments = [out_info['gm'], out_info['wm']]
time_elapsed = time.time() - start_time
print('[INFO, t={:.2f}s] \t segmented {}'.format(
time_elapsed,
image.split(sep)[-1]))
# preprocessing step: creating a mask
time_elapsed = time.time() - start_time
print(
'[INFO, t={:.2f}s] creating a mask using segments'.format(
time_elapsed))
add = math_img("img1 + img2",
img1=segments[0],
img2=segments[1])
if sub_name == 'sub-08':
full = compute_epi_mask(add, exclude_zeros=True)
else:
full = compute_epi_mask(add)
insides = compute_background_mask(full, opening=12)
union = intersect_masks([full, insides], threshold=0)
mask_file = join(preproc_dir, 'mask.nii')
union.to_filename(mask_file)
if do_coreg:
resampled_mask = resample_to_img(mask_file,
mean_t2_img,
clip=True)
rounded_resampled_mask = math_img('np.around(img1)',
img1=resampled_mask)
resampled_mask_img = join(preproc_dir, 'resampled_mask.nii')
rounded_resampled_mask.to_filename(resampled_mask_img)
else:
resampled_mask_img = mask_file
# estimation directory setup
time_elapsed = time.time() - start_time
print('[INFO, t={:.2f}s] starting estimation...'.format(time_elapsed))
recon_dir = join(cwd, 'tmp_t2', 'recon')
if not exists(recon_dir):
makedirs(recon_dir)
if do_normalise_before:
niftis_str = normed_nifti
else:
niftis_str = nifti
if do_segment == False:
mask_file = None
# estimation: t2 estimation
system(f"python3 ../scripts/qmri_t2_map.py\
-v 1\
-s {sub_name}\
-o {recon_dir}\
-n {niftis_str}\
-m {resampled_mask_img}")
recon_map = join(recon_dir, f'{sub_name}_T2map.nii.gz')
# postprocessing: normalization of reconstructed t1 map
if do_normalise_after:
postproc_dir = join(cwd, 'tmp_t2', 'postproc')
if not exists(postproc_dir):
makedirs(postproc_dir)
system('cp {} {}'.format(recon_map, postproc_dir))
time_elapsed = time.time() - start_time
print('[INFO, t={:.2f}s] normalizing reconstructed map...'.format(
time_elapsed))
image = join(postproc_dir, f'{sub_name}_T2map.nii.gz')
system('gunzip -df {}'.format(image))
image = join(postproc_dir, f'{sub_name}_T2map.nii')
out_info = to_MNI(image,
segmented=out_info.nipype_results['segment'])
norm_recon_map = out_info['anat']
# doing the plots
if do_plot:
time_elapsed = time.time() - start_time
print('\n[INFO, t={:.2f}s] plotting the map...'.format(
time_elapsed))
plot_dir = join(cwd, 'tmp_t2', 'plot')
if not exists(plot_dir):
makedirs(plot_dir)
if do_normalise_after:
t2_img = norm_recon_map
else:
t2_img = recon_map
plot_thresholded_qmap(img=t2_img,
thresh="95",
map=f"{sub_name}_T2map",
interactive=True,
coords=(10, 56, 43),
output_folder=plot_dir)
time_elapsed = time.time() - start_time
print('\n[INFO, t={:.2f}s] DONE'.format(time_elapsed))
# move derived files out and delete tmp_t2 directory
final_recon_map = join(
SAVE_TO,
f'{sub_name}_run-0{run_count+1}_space-{space}_T2map.nii.gz')
if do_normalise_after:
system('gzip {}'.format(norm_recon_map))
recon_map = norm_recon_map + '.gz'
shutil.move(recon_map, final_recon_map)
if do_plot:
for fi in listdir(plot_dir):
plot_name = join(plot_dir, fi)
final_plot_name = join(
SAVE_TO,
final_recon_map.split(sep)[-1].split('.')[0] + '_' +
fi.split('_')[-1])
shutil.move(plot_name, final_plot_name)
if not keep_tmp:
shutil.rmtree(join(SAVE_TO, 'tmp_t2'))
time_elapsed = time.time() - start_time
print('\n[INFO, t={:.2f}s] created {} \n\n'.format(
time_elapsed, final_recon_map))
run_count = run_count + 1
