def CoregisterCT2MRI(self,
subjects,
input_folder,
fixed_image='reg_run[0-9]_bc_t1'):
"""Co-registration of postoperative CT to preoperative MRI for further analyses in same space; before registration
presence of registered MRI data is ensured to avoid redundancy"""
print('\nStarting co-registration for {} subject(s)'.format(
len(subjects)))
allfiles = FileOperations.get_filelist_as_tuple(inputdir=input_folder,
subjects=subjects)
self.check_for_normalisation(subjects)
regex_complete = ['CT_', '{}_'.format(fixed_image.upper())]
included_sequences = [
x for x in list(
filter(re.compile(r"^(?!~).*").match, regex_complete))
]
file_ID_CT, file_ID_MRI = ([] for _ in range(2))
[
file_ID_CT.append(x) for x in allfiles
if 'run' not in x[0] and re.search(
r'\w+{}.'.format(included_sequences[0]), x[0], re.IGNORECASE)
and x[0].endswith('.nii')
]
[
file_ID_MRI.append(x) for x in
allfiles # for simplicity written in a second line as regexp is slightly different
if re.search(r'\w+(?!_).({}).'.format(included_sequences[1]), x[0],
re.IGNORECASE) and x[0].endswith('.nii')
]
if not file_ID_MRI:
Output.msg_box(text="Bias-corrected MRI not found!",
title="Preprocessed MRI unavailable")
return
fileIDs = list(FileOperations.inner_join(file_ID_CT, file_ID_MRI))
self.wrapper_multiprocessing(fileIDs, subjects, 'CT')
def PaCER_script(subjects, inputfolder=''):
"""wrapper script for all steps included in the PaCER algorithm"""
print("\nLead detection of {} subject(s)".format(len(subjects)))
inputfolder = cfg['folders']['nifti'] if not inputfolder else inputfolder # select default input folder
LW = LeadWorks() # load the class including necessary functions
# Look for data files containing CT imaging including the brainMask and load this into workspace
available_files = FileOperations.get_filelist_as_tuple(inputdir=inputfolder, subjects=subjects)
regex2lookfor = 'reg_' + 'run[0-9]', 'brainmask_'
file_id_CTimaging = [file_tuple for file_tuple in available_files
if re.search(r'\w.({}).'.format(regex2lookfor[0]), file_tuple[0], re.IGNORECASE)
and file_tuple[0].endswith('.nii') and 'CT' in file_tuple[0]]
file_id_brainMask = [file_tuple for file_tuple in available_files
if re.search(r'\w.({}).'.format(regex2lookfor[1]), file_tuple[0], re.IGNORECASE)
and file_tuple[0].endswith('.nii')]
if any(t > 2 for t in [len(k) for k in file_id_CTimaging]):
print("More than one files for imaging or brainmask available. Please double-check!")
return
if not file_id_brainMask:
warnings.warn(message="\tNo brain mask was found, trying to obtain a mask using ANTSpyNET routines")
regex2lookforT1 = cfg['preprocess']['normalisation']['prefix'] + 'run'
file_id_T1 = [file_tuple for file_tuple in available_files
if re.search(r'\w.({}).'.format(regex2lookforT1), file_tuple[0], re.IGNORECASE)
and 't1' in file_tuple[0] and file_tuple[0].endswith('.nii')]
if not file_id_T1:
Output.msg_box(text='No T1-sequence imaging available. BrainMask extraction impossible.',
title='T1 sequences missing")')
return
else:
T1imaging = ants.image_read(file_id_T1[0][0])
file_id_brainMask = Imaging.create_brainmask(input_folder=inputfolder, subj=''.join(subjects),
registered_images=T1imaging)
file_id_brainMask = [file_id_brainMask] if type(file_id_brainMask) == tuple else file_id_brainMask
fileID = list(FileOperations.inner_join(file_id_brainMask, file_id_CTimaging)) # joins all to single list
metal_threshold = int(cfg['lead_detection']['PaCER']['metal_threshold'])
elecModels, intensityProfiles, skelSkalms = LW.electrodeEstimation(fileID[0], threshold=metal_threshold)
elecModels, skelSkalms, intensityProfiles, _ = \
LeadProperties.estimate_hemisphere(elecModels, intensityProfiles, skelSkalms) # returns hemisphere from coords.
filename_save = os.path.join(os.path.join(inputfolder, subjects[0]), 'elecModels_' + subjects[0] + '.pkl')
with open(filename_save, "wb") as f:
pickle.dump(elecModels, f)
pickle.dump(intensityProfiles, f)
pickle.dump(skelSkalms, f)
sides = ['left', 'right']
rotation_default, rotation_mod = [{k: [] for k in sides} for _ in range(2)]
for s in sides:
rotation_default[s] = function_wrapper(subj=subjects[0], side=s)
rotation_mod[s] = Configuration.rotation_dict_mod() # creates an empty array to save modified data later
filename_save = os.path.join(os.path.join(inputfolder, subjects[0]), 'rotation_' + subjects[0] + '.pkl')
with open(filename_save, "wb") as f:
pickle.dump(rotation_default, f)
pickle.dump(rotation_mod, f)
print("Finished with lead detection!")
# TODO: it does not return to the empty command line.
return