def N4BiasCorrection_multiprocessing(self, file2rename, subj, input_folder, status):
"""Does the Bias correction taking advantage of the multicores, so that multiple subjects can be processed in
parallel; For that a list of tuples including the entire filename and the subject to be processed are entered"""
status.put(tuple([mp.current_process().name, subj, os.path.split(file2rename)[1]]))
filename_save = os.path.join(input_folder, self.cfg['preprocess']['ANTsN4']['prefix'] +
os.path.split(file2rename)[1])
# Start with N4 Bias correction for sequences specified before
original_image = ants.image_read(os.path.join(input_folder, file2rename))
rescaler_nonneg = ants.contrib.RescaleIntensity(10, 100) # to avoid values <0 causing problems w/ log data
if self.cfg['preprocess']['ANTsN4']['denoise'] == 'yes': # takes forever and therefore not used by default
original_image = ants.denoise_image(image=original_image, noise_model='Rician')
min_orig, max_orig = original_image.min(), original_image.max()
if not os.path.split(file2rename)[1].startswith(self.cfg['preprocess']['ANTsN4']['dti_prefix']):
original_image_nonneg = rescaler_nonneg.transform(original_image)
else:
original_image_nonneg = original_image
bcorr_image = n4biascorr(original_image_nonneg, mask=None,
shrink_factor=self.cfg['preprocess']['ANTsN4']['shrink-factor'],
convergence={'iters': self.cfg['preprocess']['ANTsN4']['convergence'],
'tol': self.cfg['preprocess']['ANTsN4']['threshold']},
spline_param=self.cfg['preprocess']['ANTsN4']['bspline-fitting'],
verbose=bool(self.verbose), weight_mask=None)
if not os.path.split(file2rename)[1].startswith(self.cfg['preprocess']['ANTsN4']['dti_prefix']):
rescaler = ants.contrib.RescaleIntensity(min_orig, max_orig)
bcorr_image = rescaler.transform(bcorr_image)
# difference between both images is saved for debugging purposes
diff_image = original_image - bcorr_image
FileOperations.create_folder(os.path.join(input_folder, "debug")) # only creates folder if not present
ants.image_write(diff_image, filename=os.path.join(input_folder, "debug", "diff_biasCorr_" +
os.path.split(file2rename)[1]))
spacing = self.cfg['preprocess']['registration']['resample_spacing']
bcorr_image = Imaging.resampleANTs(mm_spacing=spacing, ANTsImageObject=bcorr_image,
file_id=filename_save, method=int(self.cfg['preprocess']
['registration']
['resample_method']))
ants.image_write(bcorr_image, filename=filename_save)
def dcm2niix_multiprocessing(self, name_subj, no_subj, dcm2niix_bin,
last_idx, total_subj, status):
"""function intended to provide multiprocessing approach to speed up extraction of DICOM data to nifti files"""
modalities = ['CT', 'MRI']
if self.logfile:
log_filename = os.path.join(
ROOTDIR, 'logs', 'log_DCM2NII_' + str(no_subj + last_idx) +
time.strftime("%Y%m%d-%H%M%S"))
else:
log_filename = os.devnull
subj_outdir = os.path.join(
self.outdir,
self.cfg['folders']['prefix'] + str(no_subj + last_idx))
FileOperations.create_folder(subj_outdir)
start_time_subject = time.time()
keptfiles, deletedfiles = ([] for _ in range(2))
for mod in modalities:
status.put((name_subj, mod, no_subj, total_subj))
input_folder_name = os.path.join(self.inputdir, name_subj + mod)
# input_folder_files = [f.path for f in os.scandir(input_folder_name)
# if (f.is_dir() and ('100' in f.path or 'DICOM' in f.path or '001' in f.path))]
input_folder_files = []
[
input_folder_files.append(item)
for item in os.listdir(input_folder_name)
if (os.path.isdir(os.path.join(input_folder_name, item)) and (
'100' in item or 'DICOM' in item or '001' in item))
]
orig_stdout = sys.stdout
sys.stdout = open(log_filename, 'w')
for folder in input_folder_files:
subprocess.call(
[
dcm2niix_bin,
'-a',
'y', # anonimisation of DICOM data
'-b',
self.cfg['preprocess']['dcm2nii']['BIDSsidecar'][0],
'-z',
self.cfg['preprocess']['dcm2nii']['OutputCompression']
[0],
'-f',
self.cfg['preprocess']['dcm2nii']['OutputFileStruct'],
'-o',
subj_outdir,
'-w',
str(self.cfg['preprocess']['dcm2nii']
['NameConflicts']),
'-v',
str(self.cfg['preprocess']['dcm2nii']['Verbosity']),
'-x',
str(self.cfg['preprocess']['dcm2nii']['ReorientCrop']),
folder
],
stdout=sys.stdout,
stderr=subprocess.STDOUT)
sys.stdout.close()
sys.stdout = orig_stdout
files_kept, files_deleted = self.select_sequences(subj_outdir)
keptfiles.extend(files_kept)
deletedfiles.extend(files_deleted)
# Functions creating/updating pipeline log, which document individually all steps along with settings
log_text = "{} files successfully converted: {}, \n\nand {} deleted: {}.\nDuration: {:.2f} secs" \
.format(len(set(keptfiles)),
'\n\t{}'.format('\n\t'.join(os.path.split(x)[1] for x in sorted(set(keptfiles)))),
len(set(deletedfiles)),
'\n\t{}'.format('\n\t'.join(os.path.split(x)[1] for x in sorted(set(deletedfiles)))),
time.time() - start_time_subject)
Output.logging_routine(text=Output.split_lines(log_text),
cfg=self.cfg,
subject=self.cfg['folders']['prefix'] +
str(no_subj),
module='dcm2nii',
opt=self.cfg['preprocess']['dcm2nii'],
project="")
def ANTsCoregisterMultiprocessing(self,
file_fixed,
file_moving,
subj,
input_folder,
flag,
step,
status,
run=1):
"""Performs Co-Registration taking advantage of multicores, i.e. multiple subjects processed in parallel"""
status.put(tuple([file_fixed, file_moving, subj]))
prev_reg = glob.glob(
os.path.join(input_folder + "/" +
self.cfg['preprocess'][flag]['prefix'] + 'run*' +
os.path.basename(file_moving)))
if not prev_reg:
print('\tNo previous registration found, starting with first run')
filename_save = os.path.join(
input_folder, self.cfg['preprocess'][flag]['prefix'] + 'run' +
str(run) + '_' + os.path.basename(file_moving))
elif re.search(r'\w+{}.'.format('CT_'), file_moving,
re.IGNORECASE) and file_moving.endswith('.nii'):
print('\tNo second run for CT-MRI registrations possible.')
return
else:
allruns = [
re.search(r'\w+(run)([\d.]+)', x).group(2) for x in prev_reg
]
lastrun = int(sorted(allruns)[-1])
file_moving = os.path.join(
input_folder,
self.cfg["preprocess"]["registration"]["prefix"] + 'run' +
str(lastrun) + '_' +
os.path.basename(file_moving)) # update for second run
run = lastrun + 1
n4prefix = self.cfg['preprocess']['ANTsN4']['prefix']
basename = '{}{}'.format(n4prefix, file_moving.split(n4prefix)[1])
filename_save = os.path.join(
input_folder, self.cfg['preprocess'][flag]['prefix'] + 'run' +
str(run) + '_' + basename)
print(filename_save)
log_filename = os.path.join(
ROOTDIR, 'logs',
"log_Registration_using_ANTs_{}_run_{}_".format(subj, str(run)) +
time.strftime("%Y%m%d-%H%M%S") + '.txt')
imaging = dict()
for idx, file_id in enumerate([file_fixed, file_moving]):
sequence = ants.image_read(
file_id) # load data and resample images if necessary
imaging[idx] = Imaging.resampleANTs(
mm_spacing=self.cfg['preprocess']['registration']
['resample_spacing'],
ANTsImageObject=sequence,
file_id=file_id,
method=int(
self.cfg['preprocess']['registration']['resample_method']))
if run == 1:
metric = self.cfg['preprocess']['registration']['metric'][0]
else:
self.cfg['preprocess']['registration'][
'default_registration'] = 'yes' # TODO: must be changed if non-default works
metric = self.cfg['preprocess']['registration']['metric'][0]
if self.cfg['preprocess']['registration'][
'default_registration'] == 'yes':
registered_images = self.default_registration(imaging,
file_fixed,
file_moving,
log_filename,
metric=metric,
step=step)
else:
registered_images = self.custom_registration(
imaging, file_fixed, file_moving, input_folder + '/',
log_filename, run)
key2rename = {
'fwdtransforms':
['{}_0GenericAffineRegistration.mat'.format(step), 1],
'invtransforms': ['{}_1InvWarpMatrix.mat'.format(step), 0]
}
for key, value in key2rename.items():
Transform = ants.read_transform(registered_images[key][value[1]])
ants.write_transform(Transform,
os.path.join(input_folder, value[0]))
ants.image_write(registered_images['warpedmovout'],
filename=filename_save)
FileOperations.create_folder(os.path.join(input_folder, "debug"))
if run > 1: # Previous registrations are moved to debug-folder
prev_reg = ''.join(prev_reg) if type(
prev_reg) == list else prev_reg
filename_dest = re.sub(
r'({}run[0-9]_)+({})'.format(
self.cfg['preprocess']['registration']['prefix'],
self.cfg['preprocess']['ANTsN4']['prefix']),
'{}RUNPREV{}_{}'.format(
self.cfg['preprocess']['registration']['prefix'], lastrun,
self.cfg['preprocess']['ANTsN4']['prefix']),
os.path.basename(prev_reg))
shutil.move(prev_reg,
os.path.join(input_folder, 'debug', filename_dest))
create_mask = True
if create_mask and 't1' in file_moving and not os.path.isfile(
os.path.join(input_folder, 'brainmask_T1.nii')):
Imaging.create_brainmask(
input_folder,
subj=subj,
registered_images=registered_images['warpedmovout'])