def start_process(self):
"""starts the process linked to the module selected; that is in case of dcm2nii it runs the extraction of nifti-
files from the DICOM folder or in case of displayN4corr it displays all nifti files available in the folder"""
input2process = []
[
input2process.append(self.mOutput.item(x).text())
for x in range(self.mOutput.count())
]
if not input2process:
Output.msg_box(
text="At least one folder with data must be selected!",
title='No directory selected')
elif len(input2process) != 0 and self.option_gui == 'dcm2niix':
print('in total, {} folders were selected'.format(
len(input2process)))
preprocDCM2NII.PreprocessDCM(input2process)
elif len(
input2process) != 0 and self.option_gui == 'displayNiftiFiles':
input_with_path = []
[
input_with_path.extend(
glob.glob(self.working_dir + '/**/' + x, recursive=True))
for x in input2process
]
Imaging.load_imageviewer(
'itk-snap', input_with_path
) # to-date, only itk-snap available. could be changed
def save_cfg_dicomdir(self):
"""Function intended to save the DICOM directory once button is pressed"""
self.cfg['folders']['dicom'] = self.working_dir
Configuration.save_config(self.cfg['folders']["rootdir"], self.cfg)
Output.msg_box(
text="Folder changed in the configuration file to {}".format(
self.working_dir),
title='Changed folder')
def start_converting(self):
folderlist = []
[folderlist.append(self.mOutput.item(x).text()) for x in range(self.mOutput.count())]
print('in total, {} folders were selected'.format(len(folderlist)))
if not folderlist:
Output.msg_box(text="At least one folder with DICOM data needed!", title="No directory selected")
else:
preprocDCM2NII.PreprocessDCM(folderlist)
def view_template(self):
"""this function opens a list dialog and enables selecting NIFTI files for e.g. check the content (identical
function as in GUITabPreprocessANTs.py."""
if not self.selected_subj_Gen:
Output.msg_box(text="No image selected. Please indicate image(s) to load.", title="No templates selected")
return
else:
template_list = [FileOperations.return_full_filename(self.wdirTemplate, x) for x in self.selected_subj_Gen]
Imaging.load_imageviewer('itk-snap', template_list) # to-date, only itk-snap available. could be changed
def save_leadModel(lead_models,
intensityProfiles,
skelSkalms,
filename=''):
if not filename:
Output.msg_box(text='No filename for saving lead model provided',
title='No filename provided')
return
with open(filename, "wb") as f:
pickle.dump(lead_models, f)
pickle.dump(intensityProfiles, f)
pickle.dump(skelSkalms, f)
def run_n4Bias_corr(self):
"""wrapper to start the preprocessing, that is the GUI in which the different options for ANTs routines
are displayed"""
if not self.selected_subj_ANT:
Output.msg_box(text="No folder selected. To proceed, please indicate what folder to process. "
"(For this option, numerous folders are possible for batch processing)",
title="No subject selected")
else:
msg = "Are you sure you want to de-bias all NIFTI-files in the following folder(s):\n\n" \
"{}".format(''.join('--> {}\n'.format(c) for c in self.selected_subj_ANT))
ret = QMessageBox.question(self, "Start Debiasing", msg, QMessageBox.Yes | QMessageBox.No, QMessageBox.Yes)
if ret == QMessageBox.Yes:
N4BiasCorr.BiasCorrection().N4BiasCorrection(subjects=self.selected_subj_ANT)
def redefineDefault(self):
"""redefines which template is set as default on the right list and in the cfg-file"""
if not self.selected_subj_Gen:
Output.msg_box(text="No template selected. Please indicate new default one.", title="No data selected")
return
elif len(self.selected_subj_Gen) > 1:
Output.msg_box(text="Please select only one image as default.", title="Too many templates selected")
return
else:
default_template = [FileOperations.return_full_filename(self.wdirTemplate, x) for x in self.selected_subj_Gen]
self.cfg['folders']['default_template'] = default_template[0]
Configuration.save_config(ROOTDIR, self.cfg)
self.run_reload_files()
def run_RegisterMRI2template(self):
"""Wrapper to run the coregistration routines for the MRI (moving image) to MRI templates (fixed image)
specified in the config file using ANTs routines"""
if not self.selected_subj_ANT:
Output.msg_box(text="No folder selected. To proceed, please indicate what folder to process. "
"(For this option, numerous folders are possible for batch processing)",
title="No subject selected")
else:
msg = "Are you sure you want to coregister the preoperative imaging in the following folders:\n\n" \
"{}".format(''.join(' -> {}\n'.format(c) for c in self.selected_subj_ANT))
ret = QMessageBox.question(self, 'MessageBox', msg,
QMessageBox.Yes | QMessageBox.No, QMessageBox.Yes)
if ret == QMessageBox.Yes:
ANTspyRoutines.RegistrationANTs().CoregisterMRI2template(subjects=self.selected_subj_ANT)
def display_nifti_files(self):
"""this function enables to select NIFTI files and e.g. compare the results obtained at this step."""
if not self.selected_subj_ANT:
Output.msg_box(text="No folder selected. Please select subject.", title="No subject selected")
return
elif len(self.selected_subj_ANT) > 1:
Output.msg_box(text="Please select only one subject to avoid loading too many images",
title="Too many subjects selected")
return
else:
image_folder = os.path.join(self.cfg['folders']['nifti'], self.selected_subj_ANT[0])
self.SelectFiles = TwoListGUI(working_directory=image_folder, option_gui="displayNiftiFiles")
self.SelectFiles.show()
def CoregisterMRI2template(self, subjects):
"""Co-Registration of preoperative MRI to specific template"""
print('\nStarting Co-Registration for {} subject(s)'.format(
len(subjects)))
all_files = FileOperations.get_filelist_as_tuple(
inputdir=self.cfg['folders']['nifti'], subjects=subjects)
sequences = self.cfg['preprocess']['normalisation']['sequences'].split(
sep=',') # sequences of interest
template = glob.glob(
os.path.join(
ROOTDIR, 'ext', 'templates',
self.cfg['preprocess']['normalisation']['template_image'] +
'/*'))
fileIDs = []
for idx, seqs in enumerate(sequences):
file_template = [
x for x in template
if re.search(r'\w+.({}).'.format(seqs), x, re.IGNORECASE)
] # corresponding template
if not file_template:
Output.msg_box(
text=
"No template found. Please ensure templates are installed at './ext/templates'",
title="Template missing!"
) # TODO install templates by default!
return
regex_complete = '{}{}'.format(
self.cfg['preprocess']['ANTsN4']['prefix'], seqs)
files_subj = [
x for x in all_files
if x[0].endswith('.nii') and 'run' not in x[0]
and re.search(r'\w+(?!_).({}).'.format(regex_complete), x[0],
re.IGNORECASE)
]
fileIDs.extend(
tuple([(file_template[0], file_id, subj)
for file_id, subj in files_subj]))
if not fileIDs:
Output.msg_box(
text="No bias-corrected MRI found. Please double-check",
title="Preprocessed MRI missing")
return
self.wrapper_multiprocessing(fileIDs, subjects, 'MRI')
def check_for_complete_input(self, item_list, modalities = ['CT', 'MRI']):
"""this function ensures, that only those subjects are displayed where MRI and CT data is available in the
correct folders (surname_nameMRI or surname_nameCT)"""
available_subjects = set([re.split(r'CT|MRI', x)[0] for x in list(item_list)])
item_list_complete = []
[item_list_complete.append(subj) for subj in list(available_subjects) if
all([os.path.isdir(os.path.join(self.dicomdir, subj + y)) for y in modalities])]
if len(available_subjects) != len(item_list_complete):
incomplete = list(set(available_subjects) - set(item_list_complete))
Output.msg_box(text="There is incomplete data or directories have unknown names. Please ensure the presence"
" of two folders (surname_nameCT) and (surname_nameMRI) for:"
"\n{}".format(''.join(' -> {}\n'.format(c) for c in incomplete)),
title="Incomplete data")
return set(item_list_complete)
def run_RegisterCT2MRI(self):
"""Wrapper to run the coregistration routines for the CT (moving image) to the T1-sequence of the MRI
(fixed image) using ANTs routines"""
if not self.selected_subj_ANT:
Output.msg_box(text="No folder selected. To proceed, please indicate what folder to process. "
"(For this option, numerous folders are possible for batch processing)",
title="No subject selected")
else:
msg = "Are you sure you want to coregister the postoperative CT imaging in the following folders:\n\n" \
"{}".format(''.join(' -> {}\n'.format(c) for c in self.selected_subj_ANT))
ret = QMessageBox.question(self, 'MessageBox', msg,
QMessageBox.Yes | QMessageBox.No, QMessageBox.Yes)
if ret == QMessageBox.Yes:
ANTspyRoutines.RegistrationANTs().CoregisterCT2MRI(subjects=self.selected_subj_ANT,
input_folder=self.cfg['folders']['nifti'])
def run_ManualCorrection(self):
"""wrapper which starts the plotting routine for the detected lead which enables manual corrections"""
if len(self.selected_subj_ANT) != 1:
Output.msg_box(text="Please select one and only one subject",
title="Subjects selected")
return
else:
msg = "Are you sure you want to process the following subject:\n\n" \
"{}".format(''.join(' -> {}\n'.format(c) for c in self.selected_subj_ANT))
ret = QMessageBox.question(self, 'MessageBox', msg,
QMessageBox.Yes | QMessageBox.No,
QMessageBox.Yes)
if ret == QMessageBox.Yes:
plotElecModel.PlotRoutines(subject=self.selected_subj_ANT[0],
inputfolder=os.path.join(
self.niftidir,
self.selected_subj_ANT[0]))
def run_LeadDetectionPaCER(self):
"""wrapper to start lead detection with PaCER routines translated to python; original data can be found at:
https://github.com/adhusch/PaCER/"""
if len(self.selected_subj_ANT) > 1:
Output.msg_box(
text=
"Please select only one subject, as multiprocessing for lead detection is not intended",
title="Too many subjects selected")
return
else:
msg = "Are you sure you want to process the following subject:\n\n" \
"{}".format(''.join(' -> {}\n'.format(c) for c in self.selected_subj_ANT))
ret = QMessageBox.question(self, 'MessageBox', msg,
QMessageBox.Yes | QMessageBox.No,
QMessageBox.Yes)
if ret == QMessageBox.Yes:
LeadDetectionRoutines.PaCER_script(
subjects=self.selected_subj_ANT)
def VisualiseLeadDetection(self):
"""wrapper to start comparisons between pre- and post-processed images after N4BiasCorrection"""
if not self.selected_subj_ANT:
Output.msg_box(
text=
"No folder selected. To proceed, please select at least one.",
title="No subject selected")
return
elif len(self.selected_subj_ANT) > 1:
Output.msg_box(text="Please select only one subj.",
title="Too many subjects selected")
return
else:
image_folder = os.path.join(self.cfg['folders']['nifti'],
self.selected_subj_ANT[0])
self.SelectFiles = TwoListGUI(working_directory=image_folder,
option_gui='displayNiftiFiles')
self.SelectFiles.show()
def get_default_lead(lead_data):
"""obtains default lead properties according to the model proposed in the PaCER algorithm @ ./template"""
if lead_data[
'model'] == 'Boston Vercise Directional': # load mat-file to proceed
mat_filename = 'boston_vercise_directed.mat'
lead_model = loadmat(
os.path.join(ROOTDIR, 'ext', 'LeadDBS', mat_filename),
'r')['electrode']
default_positions = {
x: np.hstack(vals)
for x, vals in lead_model.items() if x.endswith('position')
}
default_coordinates = np.array(lead_model['coords_mm']) # in [mm]
else:
Output.msg_box(text="Lead type not yet implemented.",
title="Lead type not implemented")
return
return lead_model, default_positions, default_coordinates
def openDetails(self):
"""opens details file which has additional information on subjects """
import subprocess
fileName = os.path.join(self.cfg['folders']['nifti'],
'subjdetails.csv')
if os.path.isfile(fileName):
if sys.platform == 'linux':
subprocess.Popen(['xdg-open', ''.join(fileName)])
else:
os.system('open"%s"' % fileName)
subprocess.run(['open', fileName], check=True)
else:
Output.msg_box(
text="Subject details unavailable, creating empty csv-file!",
title="Detail file not found")
subprocess.call([
'touch',
'{}subjdetails.csv'.format(self.cfg['folders']['nifti'])
])
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 __init__(self, _folderlist):
self.logfile = True
self.cfg = Configuration.load_config(ROOTDIR)
self.DCM2NIIX_ROOT = os.path.join(ROOTDIR, 'ext', 'dcm2niix')
if not os.path.isdir(self.cfg['folders']['dicom']):
Output.msg_box(
text="Please indicate a correct folder in the main GUI",
title="Wrong folder")
else:
self.inputdir = self.cfg['folders']['dicom']
subjlist = self.create_subjlist(_folderlist)
if not os.path.isdir(self.DCM2NIIX_ROOT):
Output.msg_box(
text=
"Extracting imaging data from DICOM-files not successful because of wrong "
"folder for 'dcm2niix'.",
title="Wrong folder!")
return
if not os.path.isdir(self.cfg['folders']['nifti']):
print(
"\nDirectory for output is invalid; assuming same base and creating folder named 'nifti' therein!"
)
self.outdir = os.path.join(
os.path.split(self.inputdir)[0], 'nifti')
if not os.path.isdir(self.outdir):
os.mkdir(self.outdir)
else:
self.outdir = self.cfg['folders']['nifti']
lastsubj = self.get_index_nifti_folders(
self.outdir, prefix=self.cfg['folders']['prefix'])
self.create_csv_subjlist(subjlist, int(lastsubj))
self.convert_dcm2nii(subjlist, last_idx=int(lastsubj))
def load_leadModel(inputdir, filename):
"""Function loading results from [preprocLeadCT.py] which emulates the PaCER toolbox"""
if not inputdir:
Output.msg_box(
text="No input folder provided, please double-check!",
title="Missing input folder")
return
elif not os.path.isfile(filename):
Output.msg_box(
text=
"Models for electrode unavailable, please run detection first!",
title="Models not available")
return
else:
with open(
filename, "rb"
) as model: # roughly ea_loadreconstruction in the LeadDBS script
lead_models = pickle.load(model)
intensityProfiles = pickle.load(model)
skelSkalms = pickle.load(model)
return lead_models, intensityProfiles, skelSkalms
def show_nifti_files(self):
"""this function opens a list dialog and enables selecting NIFTI files for e.g. check the content (identical
function as in GUITabPreprocessANTs.py."""
if not self.selected_subj_Gen:
Output.msg_box(
text=
"No folder selected. To proceed, please indicate what folder to process.",
title="No subject selected")
return
elif len(self.selected_subj_Gen) > 1:
Output.msg_box(
text=
"Please select only one folder to avoid excessive image load",
title="Number of selected files")
return
else:
image_folder = os.path.join(self.cfg['folders']['nifti'],
self.selected_subj_Gen[0])
self.SelectFiles = TwoListGUI(working_directory=image_folder,
option_gui='displayNiftiFiles')
self.SelectFiles.show()
def get_settings_from_config(self):
"""function which enters the settings according to cfg variable which is loaded"""
if self.cfg == "":
print()
Output.msg_box(
title="Warning",
text=
"No default settings found, please double check the folder content. "
"Continuing with same settings.")
else:
if self.cfg["preprocess"]["dcm2nii"]["BIDSsidecar"] == 'yes':
self.rbtnBIDSy.setChecked(True)
else:
self.rbtnBIDSn.setChecked(True)
if self.cfg["preprocess"]["dcm2nii"]["OutputCompression"] == 'yes':
self.rbtnCompressiony.setChecked(True)
else:
self.rbtnCompressionn.setChecked(True)
if self.cfg["preprocess"]["dcm2nii"]["Verbosity"] == 0:
self.rbtnVerbosity0.setChecked(True)
elif self.cfg["preprocess"]["dcm2nii"]["Verbosity"] == 1:
self.rbtnVerbosity1.setChecked(True)
else:
self.rbtnVerbosity2.setChecked(True)
self.lineEditFilename.setText(
self.cfg["preprocess"]["dcm2nii"]["OutputFileStruct"])
self.lineEditIncludeFiles.setText(
self.cfg["preprocess"]["dcm2nii"]["IncludeFiles"])
if self.cfg["preprocess"]["dcm2nii"]["ReorientCrop"] == 'yes':
self.rbtnReorientCropy.setChecked(True)
else:
self.rbtnReorientCropn.setChecked(True)
def __init__(self, working_directory, _option_gui, parent=None):
super(QWidget, self).__init__(parent)
self.cfg = Configuration.load_config(ROOTDIR)
self.option_gui = _option_gui
# ============================ Different options available ============================
if self.option_gui == 'dcm2niix':
working_dir = self.cfg['folders']['dicom']
self.working_dir = working_dir if os.path.isdir(
working_dir) else os.getcwd()
options = {
'folderbox_title': "Directory (DICOM-files)",
'str_labelDir': 'DICOM DIR: {}'.format(self.working_dir),
'runBTN_label': 'Run processing'
}
elif self.option_gui == 'displayNiftiFiles':
if not working_directory:
Output.msg_box(
text="Please provide a valid folder. Terminating this GUI.",
title="No folder provided")
self.close()
return
else:
self.working_dir = working_directory
options = {
'folderbox_title': "Directory (nifti-files)",
'str_labelDir': 'subjects\' DIR: {}'.format(self.working_dir),
'runBTN_label': 'View files'
}
else:
Output.msg_box(
text=
"Please provide a valid option such as 'dcm2niix' or 'displayNiftiFiles'. "
"Terminating the GUI",
title="Wrong input as option")
self.close()
return
# ============================ Start creating layout ============================
# Create general layout
self.tot_layout = QVBoxLayout(self)
self.mid_layout = QHBoxLayout(self)
# ============================ Create upper of GUI, i.e. working directory ============================
self.label_folderbox = QGroupBox(options["folderbox_title"])
self.HBoxUpperTwoListGUI = QVBoxLayout(self.label_folderbox)
self.label_workingdir = QLabel(options["str_labelDir"])
self.HBoxUpperTwoListGUI.addWidget(self.label_workingdir)
self.btn_workingdir = QPushButton('Change working \ndirectory')
self.btn_workingdir.setFixedSize(150, 40)
self.btn_workingdir.setDisabled(True)
if self.option_gui == 'dcm2niix':
self.btn_workingdir.setEnabled(True)
self.btn_workingdir.clicked.connect(self.change_workingdir)
self.btn_savedir = QPushButton('Save directory \nto config file')
self.btn_savedir.setFixedSize(150, 40)
self.btn_savedir.setDisabled(True)
if self.option_gui == 'dcm2niix':
self.btn_savedir.setEnabled(True)
self.btn_savedir.setToolTip(
Output.split_lines(setToolTips.saveDirButton()))
self.btn_savedir.clicked.connect(self.save_cfg_dicomdir)
hlay_upper = QHBoxLayout()
hlay_upper.addWidget(self.btn_workingdir)
hlay_upper.addWidget(self.btn_savedir)
hlay_upper.addStretch(1)
self.HBoxUpperTwoListGUI.addLayout(hlay_upper)
# ==================== Create Content for Lists, i.e. input/output ====================
self.listboxInputGUITwoList = QGroupBox(
'Available items in working directory')
self.listboxInput = QVBoxLayout(self.listboxInputGUITwoList)
self.mInput = QListWidget()
self.listboxInput.addWidget(self.mInput)
self.mButtonToAvailable = QPushButton("<<")
self.mBtnMoveToAvailable = QPushButton(">")
self.mBtnMoveToSelected = QPushButton("<")
self.mButtonToSelected = QPushButton(">>")
self.mBtnUp = QPushButton("Up")
self.mBtnDown = QPushButton("Down")
self.listboxOutputGUITwoLIst = QGroupBox('Items to process')
self.listboxOutput = QVBoxLayout(self.listboxOutputGUITwoLIst)
self.mOutput = QListWidget()
self.listboxOutput.addWidget(self.mOutput)
# First column (Left side)
vlay = QVBoxLayout()
vlay.addStretch()
vlay.addWidget(self.mBtnMoveToAvailable)
vlay.addWidget(self.mBtnMoveToSelected)
vlay.addStretch()
vlay.addWidget(self.mButtonToAvailable)
vlay.addWidget(self.mButtonToSelected)
vlay.addStretch()
# Second column (Right side)
vlay2 = QVBoxLayout()
vlay2.addStretch()
vlay2.addWidget(self.mBtnUp)
vlay2.addWidget(self.mBtnDown)
vlay2.addStretch()
# ==================== Lower part of GUI, i.e. Preferences/Start estimation ====================
self.btn_preferences = QPushButton("Preferences")
self.btn_preferences.setDisabled(True)
self.btn_preferences.clicked.connect(self.settings_show)
if self.option_gui == 'dcm2niix':
self.btn_preferences.setEnabled(True)
self.btn_run_command = QPushButton(options["runBTN_label"])
if self.option_gui == 'dcm2niix':
self.btn_run_command.setToolTip(setToolTips.run_dcm2niix())
else:
self.btn_run_command.setToolTip(
setToolTips.run_CheckRegistration())
self.btn_run_command.clicked.connect(self.start_process)
hlay_bottom = QHBoxLayout()
hlay_bottom.addStretch(1)
hlay_bottom.addWidget(self.btn_preferences)
hlay_bottom.addWidget(self.btn_run_command)
hlay_bottom.addStretch()
# ==================== Set all contents to general Layout =======================
self.mid_layout.addWidget(self.listboxInputGUITwoList)
self.mid_layout.addLayout(vlay)
self.mid_layout.addWidget(self.listboxOutputGUITwoLIst)
self.mid_layout.addLayout(vlay2)
self.tot_layout.addWidget(self.label_folderbox)
self.tot_layout.addLayout(self.mid_layout)
self.tot_layout.addLayout(hlay_bottom)
try:
self.mInput.clear()
if self.option_gui == 'dcm2niix':
items = FileOperations.list_folders(self.working_dir,
prefix='')
else:
items = FileOperations.list_files_in_folder(
inputdir=self.working_dir, contains='', suffix='nii')
self.addAvailableItems(items)
except FileExistsError:
print('{} without any valid files/folders, continuing ...'.format(
self.working_dir))
self.update_buttons_status()
self.connections()
def save_cfg(self):
"""Function intended to save the DICOM directory once button is pressed"""
self.cfg['folders']['dicom'] = self.dicomdir
Configuration.save_config(self.cfg['folders']['rootdir'], self.cfg)
Output.msg_box(text="Folder changed in configuration to {}".format(self.dicomdir), title="Changed folder")
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