# running group level 1 (perform intensity normalisation, calculate response functions, average response )
elif app.args.analysis_level == "group1":
print('performing intensity normalisation')
intensitynorm_output = os.path.join(template_dir, 'inorm_output')
if not os.path.exists(intensitynorm_output):
os.mkdir(intensitynorm_output)
fa_template = os.path.join(template_dir, 'fa_template.mif')
app.checkOutputPath(fa_template)
fa_wm_mask = os.path.join(template_dir, 'fa_wm_mask.mif')
app.checkOutputPath(fa_wm_mask)
# TODO Check if outputs exist
app.gotoTempDir()
os.mkdir('inorm_input')
os.mkdir('mask_input')
# make symlinks to all dwi intensity normalisation inputs in single directory
for subj in glob.glob(os.path.join(all_subjects_dir, '*')):
os.symlink(
os.path.join(subj, 'dwi_preproc_bias.mif'),
os.path.join('inorm_input',
os.path.basename(subj) + '.mif'))
os.symlink(os.path.join(subj, 'mask.mif'),
os.path.join('mask_input',
os.path.basename(subj) + '.mif'))
run.command('dwiintensitynorm ' + 'inorm_input ' + 'mask_input ' +
intensitynorm_output + ' ' + fa_template + ' ' + fa_wm_mask +
def runGroup(output_dir):
# Check presence of all required input files before proceeding
# Pre-calculate paths of all files since many will be used in more than one location
class subjectPaths(object):
def __init__(self, label):
self.in_dwi = os.path.join(output_dir, label, 'dwi',
label + '_dwi.nii.gz')
self.in_bvec = os.path.join(output_dir, label, 'dwi',
label + '_dwi.bvec')
self.in_bval = os.path.join(output_dir, label, 'dwi',
label + '_dwi.bval')
self.in_json = os.path.join(output_dir, label, 'dwi',
label + '_dwi.json')
self.in_rf = os.path.join(output_dir, label, 'dwi',
label + '_response.txt')
self.in_connectome = os.path.join(output_dir, label, 'connectome',
label + '_connectome.csv')
self.in_mu = os.path.join(output_dir, label, 'connectome',
label + '_mu.txt')
for entry in vars(self).values():
if not os.path.exists(entry):
app.error(
'Unable to find critical subject data (expected location: '
+ entry + ')')
with open(self.in_mu, 'r') as f:
self.mu = float(f.read())
self.RF = []
with open(self.in_rf, 'r') as f:
for line in f:
self.RF.append([float(v) for v in line.split()])
self.temp_mask = os.path.join('masks', label + '.mif')
self.temp_fa = os.path.join('images', label + '.mif')
self.temp_bzero = os.path.join('bzeros', label + '.mif')
self.temp_warp = os.path.join('warps', label + '.mif')
self.temp_voxels = os.path.join('voxels', label + '.mif')
self.median_bzero = 0.0
self.dwiintensitynorm_factor = 1.0
self.RF_multiplier = 1.0
self.global_multiplier = 1.0
self.temp_connectome = os.path.join('connectomes', label + '.csv')
self.out_scale_bzero = os.path.join(
output_dir, label, 'connectome',
label + '_scalefactor_bzero.csv')
self.out_scale_RF = os.path.join(
output_dir, label, 'connectome',
label + '_scalefactor_response.csv')
self.out_connectome = os.path.join(
output_dir, label, 'connectome',
label + '_connectome_scaled.csv')
self.label = label
subject_list = [
'sub-' + sub_dir.split("-")[-1]
for sub_dir in glob.glob(os.path.join(output_dir, 'sub-*'))
]
if not subject_list:
app.error(
'No processed subject data found in output directory for group analysis'
)
subjects = []
for label in subject_list:
subjects.append(subjectPaths(label))
app.makeTempDir()
app.gotoTempDir()
# First pass through subject data in group analysis:
# - Grab DWI data (written back from single-subject analysis back into BIDS format)
# - Generate mask and FA images to be used in populate template generation
# - Generate mean b=0 image for each subject for later use
progress = app.progressBar('Importing and preparing subject data',
len(subjects))
run.function(os.makedirs, 'bzeros')
run.function(os.makedirs, 'images')
run.function(os.makedirs, 'masks')
for s in subjects:
grad_import_option = ' -fslgrad ' + s.in_bvec + ' ' + s.in_bval
run.command('dwi2mask ' + s.in_dwi + ' ' + s.temp_mask +
grad_import_option)
run.command('dwi2tensor ' + s.in_dwi + ' - -mask ' + s.temp_mask +
grad_import_option + ' | tensor2metric - -fa ' + s.temp_fa)
run.command('dwiextract ' + s.in_dwi + grad_import_option +
' - -bzero | mrmath - mean ' + s.temp_bzero + ' -axis 3')
progress.increment()
progress.done()
# First group-level calculation: Generate the population FA template
app.console(
'Generating population template for inter-subject intensity normalisation WM mask derivation'
)
run.command(
'population_template images -mask_dir masks -warp_dir warps template.mif '
'-type rigid_affine_nonlinear -rigid_scale 0.25,0.5,0.8,1.0 -affine_scale 0.7,0.8,1.0,1.0 '
'-nl_scale 0.5,0.75,1.0,1.0,1.0 -nl_niter 5,5,5,5,5 -linear_no_pause')
file.delTemporary('images')
file.delTemporary('masks')
# Second pass through subject data in group analysis:
# - Warp template FA image back to subject space & threshold to define a WM mask in subject space
# - Calculate the median subject b=0 value within this mask
# - Store this in a file, and contribute to calculation of the mean of these values across subjects
# - Contribute to the group average response function
progress = app.progressBar(
'Generating group-average response function and intensity normalisation factors',
len(subjects) + 1)
run.function(os.makedirs, 'voxels')
sum_median_bzero = 0.0
sum_RF = []
for s in subjects:
run.command('mrtransform template.mif -warp_full ' + s.temp_warp +
' - -from 2 -template ' + s.temp_bzero + ' | '
'mrthreshold - ' + s.temp_voxels + ' -abs 0.4')
s.median_bzero = float(
image.statistic(s.temp_bzero, 'median', '-mask ' + s.temp_voxels))
file.delTemporary(s.temp_bzero)
file.delTemporary(s.temp_voxels)
file.delTemporary(s.temp_warp)
sum_median_bzero += s.median_bzero
if sum_RF:
sum_RF = [[a + b for a, b in zip(one, two)]
for one, two in zip(sum_RF, s.RF)]
else:
sum_RF = s.RF
progress.increment()
file.delTemporary('bzeros')
file.delTemporary('voxels')
file.delTemporary('warps')
progress.done()
# Second group-level calculation:
# - Calculate the mean of median b=0 values
# - Calculate the mean response function, and extract the l=0 values from it
mean_median_bzero = sum_median_bzero / len(subjects)
mean_RF = [[v / len(subjects) for v in line] for line in sum_RF]
mean_RF_lzero = [line[0] for line in mean_RF]
# Third pass through subject data in group analysis:
# - Scale the connectome strengths:
# - Multiply by SIFT proportionality coefficient mu
# - Multiply by (mean median b=0) / (subject median b=0)
# - Multiply by (subject RF size) / (mean RF size)
# (needs to account for multi-shell data)
# - Write the result to file
progress = app.progressBar(
'Applying normalisation scaling to subject connectomes', len(subjects))
run.function(os.makedirs, 'connectomes')
for s in subjects:
RF_lzero = [line[0] for line in s.RF]
s.RF_multiplier = 1.0
for (mean, subj) in zip(mean_RF_lzero, RF_lzero):
s.RF_multiplier = s.RF_multiplier * subj / mean
# Don't want to be scaling connectome independently for differences in RF l=0 terms across all shells;
# use the geometric mean of the per-shell scale factors
s.RF_multiplier = math.pow(s.RF_multiplier, 1.0 / len(mean_RF_lzero))
s.bzero_multiplier = mean_median_bzero / s.median_bzero
s.global_multiplier = s.mu * s.bzero_multiplier * s.RF_multiplier
connectome = []
with open(s.in_connectome, 'r') as f:
for line in f:
connectome.append([float(v) for v in line.split()])
with open(s.temp_connectome, 'w') as f:
for line in connectome:
f.write(' '.join([str(v * s.global_multiplier)
for v in line]) + '\n')
progress.increment()
progress.done()
# Third group-level calculation: Generate the group mean connectome
# For any higher-level analysis (e.g. NBSE, computing connectome global measures, etc.),
# trying to incorporate such analysis into this particular pipeline script is likely to
# overly complicate the interface, and not actually provide much in terms of
# convenience / reproducibility guarantees. The primary functionality of this group-level
# analysis is therefore to achieve inter-subject connection density normalisation; users
# then have the flexibility to subsequently analyse the data however they choose (ideally
# based on subject classification data provided with the BIDS-compliant dataset).
progress = app.progressBar('Calculating group mean connectome',
len(subjects) + 1)
mean_connectome = []
for s in subjects:
connectome = []
with open(s.temp_connectome, 'r') as f:
for line in f:
connectome.append([float(v) for v in line.split()])
if mean_connectome:
mean_connectome = [[c1 + c2 for c1, c2 in zip(r1, r2)]
for r1, r2 in zip(mean_connectome, connectome)]
else:
mean_connectome = connectome
progress.increment()
mean_connectome = [[v / len(subjects) for v in row]
for row in mean_connectome]
progress.done()
# Write results of interest back to the output directory;
# both per-subject and group information
progress = app.progressBar('Writing results to output directory',
len(subjects) + 2)
for s in subjects:
run.function(shutil.copyfile, s.temp_connectome, s.out_connectome)
with open(s.out_scale_bzero, 'w') as f:
f.write(str(s.bzero_multiplier))
with open(s.out_scale_RF, 'w') as f:
f.write(str(s.RF_multiplier))
progress.increment()
with open(os.path.join(output_dir, 'mean_response.txt'), 'w') as f:
for row in mean_RF:
f.write(' '.join([str(v) for v in row]) + '\n')
progress.increment()
with open(os.path.join(output_dir, 'mean_connectome.csv'), 'w') as f:
for row in mean_connectome:
f.write(' '.join([str(v) for v in row]) + '\n')
progress.done()
run.command('mrconvert -stride -1,2,3,4 -fslgrad ' + bveclist[0] + ' ' + bvallist[0] + ' ' + ''.join(DWInlist) + ''.join(DWIext) + ' ' + path.toTemp('dwi.mif',True))
else:
run.command('mrconvert -stride -1,2,3,4 ' + ''.join(DWInlist) + ' ' + path.toTemp('dwi.mif',True))
else:
for idx,i in enumerate(DWInlist):
if not isdicom:
run.command('mrconvert -stride -1,2,3,4 -fslgrad ' + bveclist[idx] + ' ' + bvallist[idx] + ' ' + i + DWIext[idx] + ' ' + path.toTemp('dwi' + str(idx) + '.mif',True))
else:
run.command('mrconvert -stride -1,2,3,4 ' + i + ' ' + path.toTemp('dwi' + str(idx) + '.mif',True))
dwi_header = image.Header(path.toTemp('dwi' + str(idx) + '.mif',True))
dwi_ind_size.append([ int(s) for s in dwi_header.size() ])
miflist.append(path.toTemp('dwi' + str(idx) + '.mif',True))
DWImif = ' '.join(miflist)
run.command('mrcat -axis 3 ' + DWImif + ' ' + path.toTemp('dwi.mif',True))
app.gotoTempDir()
# get diffusion header info - check to make sure all values are valid for processing
dwi_header = image.Header(path.toTemp('dwi.mif',True))
dwi_size = [ int(s) for s in dwi_header.size() ]
grad = dwi_header.keyval()['dw_scheme']
grad = [ line for line in grad ]
grad = [ [ float(f) for f in line ] for line in grad ]
stride = dwi_header.strides()
num_volumes = 1
if len(dwi_size) == 4:
num_volumes = dwi_size[3]
bval = [i[3] for i in grad]
nvols = [i[3] for i in dwi_ind_size]
for idx,i in enumerate(DWInlist):
def runSubject(bids_dir, label, output_prefix):
output_dir = os.path.join(output_prefix, label)
if os.path.exists(output_dir):
shutil.rmtree(output_dir)
os.makedirs(output_dir)
os.makedirs(os.path.join(output_dir, 'connectome'))
os.makedirs(os.path.join(output_dir, 'dwi'))
fsl_path = os.environ.get('FSLDIR', '')
if not fsl_path:
app.error(
'Environment variable FSLDIR is not set; please run appropriate FSL configuration script'
)
flirt_cmd = fsl.exeName('flirt')
fslanat_cmd = fsl.exeName('fsl_anat')
fsl_suffix = fsl.suffix()
unring_cmd = 'unring.a64'
if not find_executable(unring_cmd):
app.console('Command \'' + unring_cmd +
'\' not found; cannot perform Gibbs ringing removal')
unring_cmd = ''
dwibiascorrect_algo = '-ants'
if not find_executable('N4BiasFieldCorrection'):
# Can't use findFSLBinary() here, since we want to proceed even if it's not found
if find_executable('fast') or find_executable('fsl5.0-fast'):
dwibiascorrect_algo = '-fsl'
app.console('Could not find ANTs program N4BiasFieldCorrection; '
'using FSL FAST for bias field correction')
else:
dwibiascorrect_algo = ''
app.warn(
'Could not find ANTs program \'N4BiasFieldCorrection\' or FSL program \'fast\'; '
'will proceed without performing DWI bias field correction')
if not app.args.parcellation:
app.error(
'For participant-level analysis, desired parcellation must be provided using the -parcellation option'
)
parc_image_path = ''
parc_lut_file = ''
mrtrix_lut_file = os.path.join(
os.path.dirname(os.path.abspath(app.__file__)), os.pardir, os.pardir,
'share', 'mrtrix3', 'labelconvert')
if app.args.parcellation == 'fs_2005' or app.args.parcellation == 'fs_2009':
if not 'FREESURFER_HOME' in os.environ:
app.error(
'Environment variable FREESURFER_HOME not set; please verify FreeSurfer installation'
)
if not find_executable('recon-all'):
app.error(
'Could not find FreeSurfer script recon-all; please verify FreeSurfer installation'
)
parc_lut_file = os.path.join(os.environ['FREESURFER_HOME'],
'FreeSurferColorLUT.txt')
if app.args.parcellation == 'fs_2005':
mrtrix_lut_file = os.path.join(mrtrix_lut_file, 'fs_default.txt')
else:
mrtrix_lut_file = os.path.join(mrtrix_lut_file, 'fs_a2009s.txt')
if app.args.parcellation == 'aal' or app.args.parcellation == 'aal2':
mni152_path = os.path.join(fsl_path, 'data', 'standard',
'MNI152_T1_1mm.nii.gz')
if not os.path.isfile(mni152_path):
app.error(
'Could not find MNI152 template image within FSL installation (expected location: '
+ mni152_path + ')')
if app.args.parcellation == 'aal':
parc_image_path = os.path.abspath(
os.path.join(os.sep, 'opt', 'aal', 'ROI_MNI_V4.nii'))
parc_lut_file = os.path.abspath(
os.path.join(os.sep, 'opt', 'aal', 'ROI_MNI_V4.txt'))
mrtrix_lut_file = os.path.join(mrtrix_lut_file, 'aal.txt')
else:
parc_image_path = os.path.abspath(
os.path.join(os.sep, 'opt', 'aal', 'ROI_MNI_V5.nii'))
parc_lut_file = os.path.abspath(
os.path.join(os.sep, 'opt', 'aal', 'ROI_MNI_V5.txt'))
mrtrix_lut_file = os.path.join(mrtrix_lut_file, 'aal2.txt')
if parc_image_path and not os.path.isfile(parc_image_path):
if app.args.atlas_path:
parc_image_path = [
parc_image_path,
os.path.join(os.path.dirname(app.args.atlas_path),
os.path.basename(parc_image_path))
]
if os.path.isfile(parc_image_path[1]):
parc_image_path = parc_image_path[1]
else:
app.error(
'Could not find parcellation image (tested locations: ' +
str(parc_image_path) + ')')
else:
app.error(
'Could not find parcellation image (expected location: ' +
parc_image_path + ')')
if not os.path.isfile(parc_lut_file):
if app.args.atlas_path:
parc_lut_file = [
parc_lut_file,
os.path.join(os.path.dirname(app.args.atlas_path),
os.path.basename(parc_lut_file))
]
if os.path.isfile(parc_lut_file[1]):
parc_lut_file = parc_lut_file[1]
else:
app.error(
'Could not find parcellation lookup table file (tested locations: '
+ str(parc_lut_file) + ')')
else:
app.error(
'Could not find parcellation lookup table file (expected location: '
+ parc_lut_file + ')')
if not os.path.exists(mrtrix_lut_file):
app.error(
'Could not find MRtrix3 connectome lookup table file (expected location: '
+ mrtrix_lut_file + ')')
app.makeTempDir()
# Need to perform an initial import of JSON data using mrconvert; so let's grab the diffusion gradient table as well
# If no bvec/bval present, need to go down the directory listing
# Only try to import JSON file if it's actually present
# direction in the acquisition they'll need to be split across multiple files
# May need to concatenate more than one input DWI, since if there's more than one phase-encode direction
# in the acquired DWIs (i.e. not just those used for estimating the inhomogeneity field), they will
# need to be stored as separate NIfTI files in the 'dwi/' directory.
dwi_image_list = glob.glob(
os.path.join(bids_dir, label, 'dwi', label) + '*_dwi.nii*')
dwi_index = 1
for entry in dwi_image_list:
# os.path.split() falls over with .nii.gz extensions; only removes the .gz
prefix = entry.split(os.extsep)[0]
if os.path.isfile(prefix + '.bval') and os.path.isfile(prefix +
'.bvec'):
prefix = prefix + '.'
else:
prefix = os.path.join(bids_dir, 'dwi')
if not (os.path.isfile(prefix + 'bval')
and os.path.isfile(prefix + 'bvec')):
app.error(
'Unable to locate valid diffusion gradient table for image \''
+ entry + '\'')
grad_import_option = ' -fslgrad ' + prefix + 'bvec ' + prefix + 'bval'
json_path = prefix + 'json'
if os.path.isfile(json_path):
json_import_option = ' -json_import ' + json_path
else:
json_import_option = ''
run.command('mrconvert ' + entry + grad_import_option +
json_import_option + ' ' +
path.toTemp('dwi' + str(dwi_index) + '.mif', True))
dwi_index += 1
# Go hunting for reversed phase-encode data dedicated to field map estimation
fmap_image_list = []
fmap_dir = os.path.join(bids_dir, label, 'fmap')
fmap_index = 1
if os.path.isdir(fmap_dir):
if app.args.preprocessed:
app.error('fmap/ directory detected for subject \'' + label +
'\' despite use of ' + option_prefix +
'preprocessed option')
fmap_image_list = glob.glob(
os.path.join(fmap_dir, label) + '_dir-*_epi.nii*')
for entry in fmap_image_list:
prefix = entry.split(os.extsep)[0]
json_path = prefix + '.json'
with open(json_path, 'r') as f:
json_elements = json.load(f)
if 'IntendedFor' in json_elements and not any(
i.endswith(json_elements['IntendedFor'])
for i in dwi_image_list):
app.console('Image \'' + entry +
'\' is not intended for use with DWIs; skipping')
continue
if os.path.isfile(json_path):
json_import_option = ' -json_import ' + json_path
# fmap files will not come with any gradient encoding in the JSON;
# therefore we need to add it manually ourselves so that mrcat / mrconvert can
# appropriately handle the table once these images are concatenated with the DWIs
fmap_image_size = image.Header(entry).size()
fmap_image_num_volumes = 1 if len(
fmap_image_size) == 3 else fmap_image_size[3]
run.command('mrconvert ' + entry + json_import_option +
' -set_property dw_scheme \"' +
'\\n'.join(['0,0,1,0'] * fmap_image_num_volumes) +
'\" ' +
path.toTemp('fmap' + str(fmap_index) +
'.mif', True))
fmap_index += 1
else:
app.warn('No corresponding .json file found for image \'' +
entry + '\'; skipping')
fmap_image_list = [
'fmap' + str(index) + '.mif' for index in range(1, fmap_index)
]
# If there's no data in fmap/ directory, need to check to see if there's any phase-encoding
# contrast within the input DWI(s)
elif len(dwi_image_list) < 2 and not app.args.preprocessed:
app.error(
'Inadequate data for pre-processing of subject \'' + label +
'\': No phase-encoding contrast in input DWIs or fmap/ directory')
dwi_image_list = [
'dwi' + str(index) + '.mif' for index in range(1, dwi_index)
]
# Import anatomical image
run.command('mrconvert ' +
os.path.join(bids_dir, label, 'anat', label + '_T1w.nii.gz') +
' ' + path.toTemp('T1.mif', True))
cwd = os.getcwd()
app.gotoTempDir()
dwipreproc_se_epi = ''
dwipreproc_se_epi_option = ''
# For automated testing, down-sampled images are used. However, this invalidates the requirements of
# both MP-PCA denoising and Gibbs ringing removal. In addition, eddy can still take a long time
# despite the down-sampling. Therefore, provide images that have been pre-processed to the stage
# where it is still only DWI, JSON & bvecs/bvals that need to be provided.
if app.args.preprocessed:
if len(dwi_image_list) > 1:
app.error(
'If DWIs have been pre-processed, then only a single DWI file should need to be provided'
)
app.console(
'Skipping MP-PCA denoising, ' +
('Gibbs ringing removal, ' if unring_cmd else '') +
'distortion correction and bias field correction due to use of ' +
option_prefix + 'preprocessed option')
run.function(os.rename, dwi_image_list[0], 'dwi.mif')
else: # Do initial image pre-processing (denoising, Gibbs ringing removal if available, distortion correction & bias field correction) as normal
# Concatenate any SE EPI images with the DWIs before denoising (& unringing), then
# separate them again after the fact
dwidenoise_input = 'dwidenoise_input.mif'
fmap_num_volumes = 0
if fmap_image_list:
run.command('mrcat ' + ' '.join(fmap_image_list) +
' fmap_cat.mif -axis 3')
for i in fmap_image_list:
file.delTemporary(i)
fmap_num_volumes = image.Header('fmap_cat.mif').size()[3]
dwidenoise_input = 'all_cat.mif'
run.command('mrcat fmap_cat.mif ' + ' '.join(dwi_image_list) +
' ' + dwidenoise_input + ' -axis 3')
file.delTemporary('fmap_cat.mif')
else:
# Even if no explicit fmap images, may still need to concatenate multiple DWI inputs
if len(dwi_image_list) > 1:
run.command('mrcat ' + ' '.join(dwi_image_list) + ' ' +
dwidenoise_input + ' -axis 3')
else:
run.function(shutil.move, dwi_image_list[0], dwidenoise_input)
for i in dwi_image_list:
file.delTemporary(i)
# Step 1: Denoise
run.command('dwidenoise ' + dwidenoise_input + ' dwi_denoised.' +
('nii' if unring_cmd else 'mif'))
if unring_cmd:
run.command('mrinfo ' + dwidenoise_input +
' -json_keyval input.json')
file.delTemporary(dwidenoise_input)
# Step 2: Gibbs ringing removal (if available)
if unring_cmd:
run.command(unring_cmd + ' dwi_denoised.nii dwi_unring' +
fsl_suffix + ' -n 100')
file.delTemporary('dwi_denoised.nii')
unring_output_path = fsl.findImage('dwi_unring')
run.command('mrconvert ' + unring_output_path +
' dwi_unring.mif -json_import input.json')
file.delTemporary(unring_output_path)
file.delTemporary('input.json')
# If fmap images and DWIs have been concatenated, now is the time to split them back apart
dwipreproc_input = 'dwi_unring.mif' if unring_cmd else 'dwi_denoised.mif'
if fmap_num_volumes:
cat_input = 'dwi_unring.mif' if unring_cmd else 'dwi_denoised.mif'
dwipreproc_se_epi = 'se_epi.mif'
run.command('mrconvert ' + cat_input + ' ' + dwipreproc_se_epi +
' -coord 3 0:' + str(fmap_num_volumes - 1))
cat_num_volumes = image.Header(cat_input).size()[3]
run.command('mrconvert ' + cat_input +
' dwipreproc_in.mif -coord 3 ' +
str(fmap_num_volumes) + ':' + str(cat_num_volumes - 1))
file.delTemporary(dwipreproc_input)
dwipreproc_input = 'dwipreproc_in.mif'
dwipreproc_se_epi_option = ' -se_epi ' + dwipreproc_se_epi
# Step 3: Distortion correction
run.command('dwipreproc ' + dwipreproc_input +
' dwi_preprocessed.mif -rpe_header' +
dwipreproc_se_epi_option)
file.delTemporary(dwipreproc_input)
if dwipreproc_se_epi:
file.delTemporary(dwipreproc_se_epi)
# Step 4: Bias field correction
if dwibiascorrect_algo:
run.command('dwibiascorrect dwi_preprocessed.mif dwi.mif ' +
dwibiascorrect_algo)
file.delTemporary('dwi_preprocessed.mif')
else:
run.function(shutil.move, 'dwi_preprocessed.mif', 'dwi.mif')
# No longer branching based on whether or not -preprocessed was specified
# Step 5: Generate a brain mask for DWI
run.command('dwi2mask dwi.mif dwi_mask.mif')
# Step 6: Perform brain extraction on the T1 image in its original space
# (this is necessary for histogram matching prior to registration)
# Use fsl_anat script
run.command('mrconvert T1.mif T1.nii -stride -1,+2,+3')
run.command(fslanat_cmd + ' -i T1.nii --noseg --nosubcortseg')
run.command('mrconvert ' +
fsl.findImage('T1.anat' + os.sep + 'T1_biascorr_brain_mask') +
' T1_mask.mif -datatype bit')
run.command('mrconvert ' +
fsl.findImage('T1.anat' + os.sep + 'T1_biascorr_brain') +
' T1_biascorr_brain.mif')
file.delTemporary('T1.anat')
# Step 7: Generate target images for T1->DWI registration
run.command('dwiextract dwi.mif -bzero - | '
'mrcalc - 0.0 -max - | '
'mrmath - mean -axis 3 dwi_meanbzero.mif')
run.command(
'mrcalc 1 dwi_meanbzero.mif -div dwi_mask.mif -mult - | '
'mrhistmatch - T1_biascorr_brain.mif dwi_pseudoT1.mif -mask_input dwi_mask.mif -mask_target T1_mask.mif'
)
run.command(
'mrcalc 1 T1_biascorr_brain.mif -div T1_mask.mif -mult - | '
'mrhistmatch - dwi_meanbzero.mif T1_pseudobzero.mif -mask_input T1_mask.mif -mask_target dwi_mask.mif'
)
# Step 8: Perform T1->DWI registration
# Note that two registrations are performed: Even though we have a symmetric registration,
# generation of the two histogram-matched images means that you will get slightly different
# answers depending on which synthesized image & original image you use.
run.command(
'mrregister T1_biascorr_brain.mif dwi_pseudoT1.mif -type rigid -mask1 T1_mask.mif -mask2 dwi_mask.mif -rigid rigid_T1_to_pseudoT1.txt'
)
file.delTemporary('T1_biascorr_brain.mif')
run.command(
'mrregister T1_pseudobzero.mif dwi_meanbzero.mif -type rigid -mask1 T1_mask.mif -mask2 dwi_mask.mif -rigid rigid_pseudobzero_to_bzero.txt'
)
file.delTemporary('dwi_meanbzero.mif')
run.command(
'transformcalc rigid_T1_to_pseudoT1.txt rigid_pseudobzero_to_bzero.txt average rigid_T1_to_dwi.txt'
)
file.delTemporary('rigid_T1_to_pseudoT1.txt')
file.delTemporary('rigid_pseudobzero_to_bzero.txt')
run.command(
'mrtransform T1.mif T1_registered.mif -linear rigid_T1_to_dwi.txt')
file.delTemporary('T1.mif')
# Note: Since we're using a mask from fsl_anat (which crops the FoV), but using it as input to 5ttge fsl
# (which is receiving the raw T1), we need to resample in order to have the same dimensions between these two
run.command(
'mrtransform T1_mask.mif T1_mask_registered.mif -linear rigid_T1_to_dwi.txt -template T1_registered.mif -interp nearest'
)
file.delTemporary('T1_mask.mif')
# Step 9: Generate 5TT image for ACT
run.command(
'5ttgen fsl T1_registered.mif 5TT.mif -mask T1_mask_registered.mif')
file.delTemporary('T1_mask_registered.mif')
# Step 10: Estimate response functions for spherical deconvolution
run.command(
'dwi2response dhollander dwi.mif response_wm.txt response_gm.txt response_csf.txt -mask dwi_mask.mif'
)
# Step 11: Determine whether we are working with single-shell or multi-shell data
shells = [
int(round(float(value)))
for value in image.mrinfo('dwi.mif', 'shellvalues').strip().split()
]
multishell = (len(shells) > 2)
# Step 12: Perform spherical deconvolution
# Use a dilated mask for spherical deconvolution as a 'safety margin' -
# ACT should be responsible for stopping streamlines before they reach the edge of the DWI mask
run.command('maskfilter dwi_mask.mif dilate dwi_mask_dilated.mif -npass 3')
if multishell:
run.command(
'dwi2fod msmt_csd dwi.mif response_wm.txt FOD_WM.mif response_gm.txt FOD_GM.mif response_csf.txt FOD_CSF.mif '
'-mask dwi_mask_dilated.mif -lmax 10,0,0')
file.delTemporary('FOD_GM.mif')
file.delTemporary('FOD_CSF.mif')
else:
# Still use the msmt_csd algorithm with single-shell data: Use hard non-negativity constraint
# Also incorporate the CSF response to provide some fluid attenuation
run.command(
'dwi2fod msmt_csd dwi.mif response_wm.txt FOD_WM.mif response_csf.txt FOD_CSF.mif '
'-mask dwi_mask_dilated.mif -lmax 10,0')
file.delTemporary('FOD_CSF.mif')
# Step 13: Generate the grey matter parcellation
# The necessary steps here will vary significantly depending on the parcellation scheme selected
run.command(
'mrconvert T1_registered.mif T1_registered.nii -stride +1,+2,+3')
if app.args.parcellation == 'fs_2005' or app.args.parcellation == 'fs_2009':
# Run FreeSurfer pipeline on this subject's T1 image
run.command('recon-all -sd ' + app.tempDir +
' -subjid freesurfer -i T1_registered.nii')
run.command('recon-all -sd ' + app.tempDir +
' -subjid freesurfer -all')
# Grab the relevant parcellation image and target lookup table for conversion
parc_image_path = os.path.join('freesurfer', 'mri')
if app.args.parcellation == 'fs_2005':
parc_image_path = os.path.join(parc_image_path, 'aparc+aseg.mgz')
else:
parc_image_path = os.path.join(parc_image_path,
'aparc.a2009s+aseg.mgz')
# Perform the index conversion
run.command('labelconvert ' + parc_image_path + ' ' + parc_lut_file +
' ' + mrtrix_lut_file + ' parc_init.mif')
if app.cleanup:
run.function(shutil.rmtree, 'freesurfer')
# Fix the sub-cortical grey matter parcellations using FSL FIRST
run.command('labelsgmfix parc_init.mif T1_registered.mif ' +
mrtrix_lut_file + ' parc.mif')
file.delTemporary('parc_init.mif')
elif app.args.parcellation == 'aal' or app.args.parcellation == 'aal2':
# Can use MNI152 image provided with FSL for registration
run.command(flirt_cmd + ' -ref ' + mni152_path +
' -in T1_registered.nii -omat T1_to_MNI_FLIRT.mat -dof 12')
run.command('transformconvert T1_to_MNI_FLIRT.mat T1_registered.nii ' +
mni152_path + ' flirt_import T1_to_MNI_MRtrix.mat')
file.delTemporary('T1_to_MNI_FLIRT.mat')
run.command(
'transformcalc T1_to_MNI_MRtrix.mat invert MNI_to_T1_MRtrix.mat')
file.delTemporary('T1_to_MNI_MRtrix.mat')
run.command('mrtransform ' + parc_image_path +
' AAL.mif -linear MNI_to_T1_MRtrix.mat '
'-template T1_registered.mif -interp nearest')
file.delTemporary('MNI_to_T1_MRtrix.mat')
run.command('labelconvert AAL.mif ' + parc_lut_file + ' ' +
mrtrix_lut_file + ' parc.mif')
file.delTemporary('AAL.mif')
else:
app.error('Unknown parcellation scheme requested: ' +
app.args.parcellation)
file.delTemporary('T1_registered.nii')
# Step 14: Generate the tractogram
# If not manually specified, determine the appropriate number of streamlines based on the number of nodes in the parcellation:
# mean edge weight of 1,000 streamlines
# A smaller FOD amplitude threshold of 0.06 (default 0.1) is used for tracking due to the use of the msmt_csd
# algorithm, which imposes a hard rather than soft non-negativity constraint
num_nodes = int(image.statistic('parc.mif', 'max'))
num_streamlines = 1000 * num_nodes * num_nodes
if app.args.streamlines:
num_streamlines = app.args.streamlines
run.command(
'tckgen FOD_WM.mif tractogram.tck -act 5TT.mif -backtrack -crop_at_gmwmi -cutoff 0.06 -maxlength 250 -power 0.33 '
'-select ' + str(num_streamlines) + ' -seed_dynamic FOD_WM.mif')
# Step 15: Use SIFT2 to determine streamline weights
fd_scale_gm_option = ''
if not multishell:
fd_scale_gm_option = ' -fd_scale_gm'
run.command(
'tcksift2 tractogram.tck FOD_WM.mif weights.csv -act 5TT.mif -out_mu mu.txt'
+ fd_scale_gm_option)
# Step 16: Generate a TDI (to verify that SIFT2 has worked correctly)
with open('mu.txt', 'r') as f:
mu = float(f.read())
run.command(
'tckmap tractogram.tck -tck_weights_in weights.csv -template FOD_WM.mif -precise - | '
'mrcalc - ' + str(mu) + ' -mult tdi.mif')
# Step 17: Generate the connectome
# Only provide the standard density-weighted connectome for now
run.command(
'tck2connectome tractogram.tck parc.mif connectome.csv -tck_weights_in weights.csv'
)
file.delTemporary('weights.csv')
# Move necessary files to output directory
run.function(
shutil.copy, 'connectome.csv',
os.path.join(output_dir, 'connectome', label + '_connectome.csv'))
run.command('mrconvert dwi.mif ' +
os.path.join(output_dir, 'dwi', label + '_dwi.nii.gz') +
' -export_grad_fsl ' +
os.path.join(output_dir, 'dwi', label + '_dwi.bvec') + ' ' +
os.path.join(output_dir, 'dwi', label + '_dwi.bval') +
' -json_export ' +
os.path.join(output_dir, 'dwi', label + '_dwi.json'))
run.command('mrconvert tdi.mif ' +
os.path.join(output_dir, 'dwi', label + '_tdi.nii.gz'))
run.function(shutil.copy, 'mu.txt',
os.path.join(output_dir, 'connectome', label + '_mu.txt'))
run.function(shutil.copy, 'response_wm.txt',
os.path.join(output_dir, 'dwi', label + '_response.txt'))
# Manually wipe and zero the temp directory (since we might be processing more than one subject)
os.chdir(cwd)
if app.cleanup:
app.console('Deleting temporary directory ' + app.tempDir)
# Can't use run.function() here; it'll try to write to the log file that resides in the temp directory just deleted
shutil.rmtree(app.tempDir)
else:
app.console('Contents of temporary directory kept, location: ' +
app.tempDir)
app.tempDir = ''