def _generateQCForRois(self, space, roi_list):
for roi in roi_list:
roi_name = self._extractFileName(roi,
remove_extension=True,
extension_count=2)
image_files_base = os.path.join(self.getBaseDirectory(),
'QC_LesionLoad', space, roi_name)
generateQCPage('LL_%s' % (roi_name), image_files_base)
def runBrainExtraction(self, anatomical_id, lesion_mask_id):
# Skip this step if user has already performed brain extraction
if self.controller.b_brain_extraction.get() == True or self.skip:
self.incrementStage()
return False
image_files_base = os.path.join(self.getBaseDirectory(),
'QC_BrainExtractions')
self.logger.info('Brain extraction has been initiated')
for subject in self.subjects:
try:
anatomical_file_path, lesion_files = self._setSubjectSpecificPaths_1(
subject, anatomical_id, lesion_mask_id)
((t1_mgz, seg_file), bet_brain_file,
wm_mask_file) = self._setSubjectSpecificPaths_2(subject)
self.com.runBet(
anatomical_file_path,
os.path.join(self.getIntermediatePath(subject),
subject + '_Brain'))
image_path = os.path.join(image_files_base,
subject + '_BET.png')
self.com.runFslEyes(anatomical_file_path, bet_brain_file,
image_path)
except:
self.logger.debug(traceback.format_exc())
pass
html_file_path = generateQCPage('BET', image_files_base)
self.printQCPageUrl('Brain extraction', html_file_path)
self.logger.info('Brain extraction completed for all subjects')
def runLesionLoadCalculation(self, anatomical_id, lesion_mask_id):
if self.stage in (6, 7):
if self.controller.b_own_rois.get() == True:
space = 'custom'
template_brain = self.controller.sv_user_brain_template.get()
if self.stage == 6:
self.runReg(template_brain, space, anatomical_id,
lesion_mask_id)
if self.stage == 7:
roi_list = self.controller.user_roi_paths
self._runLesionLoadCalculationHelper(
space, roi_list, anatomical_id, lesion_mask_id)
self.incrementStage()
else:
self.incrementStage(2)
if self.stage in (8, 9):
if self.controller.b_default_rois.get() == True:
space = 'MNI152'
template_brain = os.path.join(
self.controller.getProjectDirectory(), 'ROIs',
'MNI152_T1_2mm_brain.nii.gz')
if self.stage == 8:
self.runReg(template_brain, space, anatomical_id,
lesion_mask_id)
if self.stage == 9:
roi_list = self.controller.default_roi_paths
self._runLesionLoadCalculationHelper(
space, roi_list, anatomical_id, lesion_mask_id)
self.incrementStage()
else:
self.incrementStage(2)
if self.stage in (10, 11):
if self.controller.b_freesurfer_rois.get() == True:
space = 'FS'
template_brain = ''
if self.stage == 10:
self.runReg(template_brain, space, anatomical_id,
lesion_mask_id)
if self.stage == 11:
self.runLesionLoadCalculationFS(space, anatomical_id,
lesion_mask_id)
self.incrementStage()
else:
self.incrementStage(2)
if self.stage == 12:
image_files_base = os.path.join(self.getBaseDirectory(),
'QC_Registrations', space)
html_file_path = generateQCPage('Registration', image_files_base)
self.logger.info(
'Lesion Load Calculation completed for all subjects')
self.printQCPageUrl('LL Calculation', html_file_path, pause=False)
self.incrementStage()
def _generateQCForRois(self, space, roi_list):
for roi in roi_list:
roi_name = self._extractFileName(roi,
remove_extension=True,
extension_count=2)
image_files_base = os.path.join(self.getBaseDirectory(),
'QC_LesionLoad', space, roi_name)
html_file_path = generateQCPage('LL_%s' % (roi_name),
image_files_base)
self.printQCPageUrl('%s-Lesion Load' % roi_name,
html_file_path,
pause=False)
def createQCPage(self):
self.logger.info('Visual QC module has been initiated.')
images_dir = os.path.join(self.getBaseDirectory(), 'QC_Lesions')
os.makedirs(images_dir)
for subject in self.subjects:
anatomical_file_path, lesion_files = self.getT1NLesionFromInput(subject)
for counter, lesion_file in enumerate(lesion_files):
cog = self.com.runFslStats(lesion_file, '-C')
x,y,z=cog.split(' ')
x=int(x)
y=int(y)
z=int(z)
output_image_path = os.path.join(self.getBaseDirectory(), 'QC_Lesions', '%s_Lesion%d.png'%(subject, counter+1))
self.com.runFslEyes2(anatomical_file_path[0], lesion_file, '', x, y, z, output_image_path)
html_file_path = generateQCPage('Lesions', images_dir)
self.printQCPageUrl('createQCPage', html_file_path, pause=False)
self.logger.info('QC Page generation completed')
def runLesionCorrection(self, anatomical_id, lesion_mask_id):
self.logger.info('Lesion correction has been initiated.')
max_lesions = 0
subject_info_all = []
for subject in self.subjects:
subject_info = [subject]
anatomical_file_path, lesion_files = self._setSubjectSpecificPaths_1(
subject, anatomical_id, lesion_mask_id)
((t1_mgz, seg_file), bet_brain_file,
wm_mask_file) = self._setSubjectSpecificPaths_2(subject)
if not self.com.runFslMathToCheckInSameSpace(
wm_mask_file, lesion_files[0],
os.path.join(self.getIntermediatePath(subject),
subject + '_corrWM')):
self.logger.info(
'Check Image Orientations for T1 and Lesion Mask. Skipping Subject: %s'
% subject)
subject_info.append('Skipped')
continue
# assign the new wm seg file (with lesion removed) to corrWM
corrected_wm_file = os.path.join(self.getIntermediatePath(subject),
subject + '_corrWM.nii.gz')
output_file = os.path.join(self.getIntermediatePath(subject),
subject + '_NormRangeWM')
# set values for healthy WM removal
# multiply WM mask by intensity normalized T1
self.com.runFslWithArgs(anatomical_file_path, corrected_wm_file,
output_file, '-mul')
wm_mean = self.com.runFslStats(output_file, '-M')
lesion_files_count = len(lesion_files)
if max_lesions < lesion_files_count:
max_lesions = lesion_files_count
self.logger.debug('Updated num of max lesions : ' +
str(max_lesions))
total_native_brain_volume = self.com.runBrainVolume(bet_brain_file)
subject_info.append(total_native_brain_volume)
subject_info.append(wm_mean)
for counter, lesion_file in enumerate(lesion_files):
# calculate original and white matter adjusted lesion volumes
original_lesion_vol = self.com.runBrainVolume(lesion_file)
skip_subject, corrected_lesion_volume, wm_adjusted_lesion = self._lesionCorrection(
subject, counter + 1, wm_mean, anatomical_file_path,
lesion_file)
if skip_subject:
self.logger.log(
'Check Image Orientations for T1 and Lesion Mask. Skipping Subject: '
+ subject)
subject_info.append('Skipped')
continue
volume_removed = original_lesion_vol - corrected_lesion_volume
percent_removed = volume_removed * 1.0 / total_native_brain_volume
#determine side of lesion
#this gets the center of gravity of the lesion using the mni coord and then extracts the first char of the X coordinate
lesion_side = self.com.runFslStats(lesion_file, '-c')
subject_info.append(lesion_side)
subject_info.append(original_lesion_vol)
subject_info.append(corrected_lesion_volume)
subject_info.append(volume_removed)
subject_info.append(percent_removed)
cog = self.com.runFslStats(lesion_file, '-C')
x, y, z = cog.split(' ')
x = int(x)
y = int(y)
z = int(z)
output_image_path = os.path.join(
self.getBaseDirectory(), 'QC_LesionCorrection',
'%s_WMAdjLesion%d.png' % (subject, counter + 1))
self.com.runFslEyes2(anatomical_file_path, lesion_file,
wm_adjusted_lesion, x, y, z,
output_image_path)
subject_info_all.append(subject_info)
header = [
'Subject', 'Total_Native_Brain_Volume',
'Mean_White_Matter_Intensity'
]
for lesion_counter in range(max_lesions):
lesion_num = str(lesion_counter + 1)
header.append('Lesion%s_Hemisphere' % lesion_num)
header.append('Lesion%s_Original_Lesion_Volume' % lesion_num)
header.append('Lesion%s_Corrected_Lesion_Volume' % lesion_num)
header.append('Lesion%s_Volume_Removed' % lesion_num)
header.append('Lesion%s_Percent_Removed' % lesion_num)
# Write data to the csv file
subject_info_with_header = [header] + subject_info_all
self.com.runAppendToCSV(
subject_info_with_header,
os.path.join(self.getBaseDirectory(),
'lesion_correction_database.csv'))
image_files_base = os.path.join(self.getBaseDirectory(),
'QC_LesionCorrection')
html_file_path = generateQCPage('Lesions', image_files_base)
self.printQCPageUrl('Lesion Correction', html_file_path)
self.logger.info('Lesion correction completed for all subjects')
lesion_mask_id = 'WMAdjusted'
return lesion_mask_id
def runReg(self, template_brain, space, anatomical_id, lesion_mask_id):
self.logger.info(
'Registration to either default or user-input ROI space has been initiated.'
)
if space == 'MNI152' or space == 'custom':
#same template brain for all subject; passed in as template_brain
for subject in self.subjects:
anatomical_file_path, lesion_files = self._setSubjectSpecificPaths_1(
subject, anatomical_id, lesion_mask_id)
((t1_mgz, seg_file), bet_brain_file,
wm_mask_file) = self._setSubjectSpecificPaths_2(subject)
reg_brain_file = os.path.join(
self.getIntermediatePath(subject),
'%s_Reg_brain_%s' % (subject, space))
reg_file = os.path.join(self.getIntermediatePath(subject),
'%s_Reg_%s.mat' % (subject, space))
self.com.runFlirt(bet_brain_file, template_brain,
reg_brain_file, reg_file)
out_image_path = os.path.join(self.getBaseDirectory(),
'QC_Registrations', space,
'%s_Reg.png' % subject)
cmd = 'fsleyes render -hl --hideCursor -of %s %s %s -cm yellow -a 90' % (
out_image_path, template_brain, reg_brain_file + '.nii.gz')
self.com.runRawCommand(cmd)
if space == 'FS':
# new template brain for each subject
for subject in self.subjects:
anatomical_file_path, lesion_files = self._setSubjectSpecificPaths_1(
subject, anatomical_id, lesion_mask_id)
((t1_mgz, seg_file), bet_brain_file,
wm_mask_file) = self._setSubjectSpecificPaths_2(subject)
template_brain = os.path.join(
self.getIntermediatePath(subject),
'%s_FST1.nii.gz' % subject)
self.com.runMriConvert(t1_mgz, template_brain)
# perform registration to FS Space for each subject to get transformation matrix
reg_file = os.path.join(self.getIntermediatePath(subject),
'%s_T12FS.xfm' % subject)
reg_brain_file = os.path.join(
self.getIntermediatePath(subject), '%s_T12FS' % subject)
self.com.runFlirt(anatomical_file_path, template_brain,
reg_brain_file, reg_file)
output_image_path = os.path.join(self.getBaseDirectory(),
'QC_Registrations', 'FS',
'%s_Reg.png' % subject)
cmd = 'fsleyes render -hl --hideCursor -of %s %s %s -cm yellow -a 90' % (
output_image_path, template_brain,
reg_brain_file + '.nii.gz')
self.com.runRawCommand(cmd)
# invert transformation matrix
xfm_inverse_file = os.path.join(
self.getIntermediatePath(subject),
'%s_T12FS_inv.xfm' % subject)
cmd = 'convert_xfm -omat %s %s;' % (xfm_inverse_file, reg_file)
self.com.runRawCommand(cmd)
image_files_base = os.path.join(self.getBaseDirectory(),
'QC_Registrations', space)
html_file_path = generateQCPage('Registration', image_files_base)
self.printQCPageUrl('Registrations', html_file_path)
self.logger.info(
'Registration to either default or user-input ROI space has been completed for all subjects.'
)
def runReg(self, template_brain, space, anatomical_id, lesion_mask_id):
self.logger.info('Performing registration...')
if space == 'MNI152' or space == 'custom':
#same template brain for all subject; passed in as template_brain
for subject in self.subjects:
anatomical_file_path, lesion_files = self._setSubjectSpecificPaths_1(
subject, anatomical_id, lesion_mask_id)
((t1_mgz, seg_file), bet_brain_file,
wm_mask_file) = self._setSubjectSpecificPaths_2(subject)
reg_brain_file = os.path.join(
self.getIntermediatePath(subject),
'%s_Reg_brain_%s' % (subject, space))
reg_file = os.path.join(self.getIntermediatePath(subject),
'%s_Reg_%s.mat' % (subject, space))
self.com.runFlirt(bet_brain_file, template_brain,
reg_brain_file, reg_file)
out_image_path = os.path.join(self.getBaseDirectory(),
'QC_Registrations', space,
'%s_Reg.png' % subject)
self.com.runFslEyes(out_image_path,
reg_brain_file + '.nii.gz',
options='')
if space == 'FS':
# new template brain for each subject
for subject in self.subjects:
anatomical_file_path, lesion_files = self._setSubjectSpecificPaths_1(
subject, anatomical_id, lesion_mask_id)
((t1_mgz, seg_file), bet_brain_file,
wm_mask_file) = self._setSubjectSpecificPaths_2(subject)
template_brain = os.path.join(
self.getIntermediatePath(subject),
'%s_FST1.nii.gz' % subject)
self.com.runMriConvert(t1_mgz, template_brain)
# perform registration to FS Space for each subject to get transformation matrix
reg_file = os.path.join(self.getIntermediatePath(subject),
'%s_T12FS.xfm' % subject)
reg_brain_file = os.path.join(
self.getIntermediatePath(subject), '%s_T12FS' % subject)
#cmd = 'flirt -in %s -ref %s -omat %s -out %s;'%(anatomical_file_path, template_brain, reg_file, reg_brain_file)
#self.com.runRawCommand(cmd)
self.com.runFlirt(anatomical_file_path, template_brain,
reg_brain_file, reg_file)
output_image_path = os.path.join(self.getBaseDirectory(),
'QC_Registrations', 'FS',
'%s_Reg.png' % subject)
self.com.runFslEyes(out_image_path,
reg_brain_file + '.nii.gz',
options='')
#self.com.runFslEyes(reg_brain_file, output_image_path=output_image_path, options='')
# invert transformation matrix
xfm_inverse_file = os.path.join(
self.getIntermediatePath(subject),
'%s_T12FS_inv.xfm' % subject)
cmd = 'convert_xfm -omat %s %s;' % (xfm_inverse_file, reg_file)
self.com.runRawCommand(cmd)
image_files_base = os.path.join(self.getBaseDirectory(),
'QC_Registrations', space)
generateQCPage('Registration', image_files_base)