def implement(self):
tt5 = self.getRegistrationImage("tt5", "register")
seed_gmwmi = self.getMaskingImage("tt5", ["register", "5tt2gmwmi"])
brodmann = self.getRegistrationImage("brodmann", "resample")
norm = self.getRegistrationImage("norm", "resample")
mask253 = self.getMaskingImage('aparc_aseg', ['253', 'mask'])
mask1024= self.getMaskingImage('aparc_aseg', ['1024', 'mask'])
dwi = self.getUpsamplingImage('dwi', 'upsample')
bFile = self.getUpsamplingImage('grad', None, 'b')
mask = self.getRegistrationImage('mask', 'resample')
#tensor part
tckDet = self.__tckgenTensor(dwi, self.buildName(dwi, 'tensor_det', 'tck'), mask, tt5, seed_gmwmi, bFile, 'Tensor_Det')
tckDetConnectome = self.__tck2connectome(tckDet, brodmann, self.buildName(tckDet, 'connectome', 'csv'))
tckDetConnectomeNormalize = self.__normalizeConnectome(tckDetConnectome, self.buildName(tckDetConnectome, 'normalize', 'csv'))
mriutil.plotConnectome(tckDetConnectomeNormalize, self.buildName(tckDetConnectomeNormalize, None, "png"))
tckDetRoi = self.__tckedit(tckDet, mask253, self.buildName(tckDet, 'roi','tck'))
tckDetRoiTrk = mriutil.tck2trk(tckDetRoi, norm , self.buildName(tckDetRoi, None, 'trk'))
tckProb = self.__tckgenTensor(dwi, self.buildName(dwi, 'tensor_prob', 'tck'), mask, tt5, seed_gmwmi, bFile, 'Tensor_Prob')
tckProbConnectome = self.__tck2connectome(tckProb, brodmann, self.buildName(tckProb, 'connectome', 'csv'))
tckProbConnectomeNormalize = self.__normalizeConnectome(tckProbConnectome, self.buildName(tckProbConnectome, 'normalize', 'csv'))
mriutil.plotConnectome(tckProbConnectomeNormalize, self.buildName(tckProbConnectomeNormalize, None, "png"))
tckProbRoi = self.__tckedit(tckProb, mask253, self.buildName(tckProb, 'roi','tck'))
tckProbRoiTrk = mriutil.tck2trk(tckProbRoi, norm , self.buildName(tckProbRoi, None, 'trk'))
#HARDI part
csd = self.getHardimrtrixImage('dwi','csd')
hardiTck = self.__tckgenHardi(csd, self.buildName(csd, 'hardi_prob', 'tck'), tt5)
hardiTckConnectome = self.__tck2connectome(hardiTck, brodmann, self.buildName(hardiTck, 'connectome', 'csv'))
hardiTckConnectomeNormalize = self.__normalizeConnectome(hardiTckConnectome, self.buildName(hardiTckConnectome, 'normalize', 'csv'))
mriutil.plotConnectome(hardiTckConnectomeNormalize, self.buildName(hardiTckConnectomeNormalize, None, "png"))
hardiTckRoi = self.__tckedit(hardiTck, mask253, self.buildName(hardiTck, 'roi','tck'))
tckgenRoiTrk = mriutil.tck2trk(hardiTckRoi, norm , self.buildName(hardiTckRoi, None, 'trk'))
tcksift = self.__tcksift(hardiTck, csd)
tcksiftConnectome = self.__tck2connectome(tcksift, brodmann, self.buildName(tcksift, 'connectome', 'csv'))
tcksiftConnectomeNormalize = self.__normalizeConnectome(tcksiftConnectome, self.buildName(tcksiftConnectome, 'normalize', 'csv'))
mriutil.plotConnectome(tcksiftConnectomeNormalize, self.buildName(tcksiftConnectomeNormalize, None, "png"))
tcksiftRoi = self.__tckedit(tcksift, mask253, self.buildName(tcksift, 'roi', 'tck'))
tcksiftRoiTrk = mriutil.tck2trk(tcksiftRoi, norm , self.buildName(tcksiftRoi, None, 'trk'))
#create PNG
if self.get('general', 'vtk_available'):
mriutil.createVtkPng(tckDetRoiTrk, norm, mask253)
mriutil.createVtkPng(tckProbRoiTrk, norm, mask253)
mriutil.createVtkPng(tckgenRoiTrk, norm, mask253)
mriutil.createVtkPng(tcksiftRoiTrk, norm, mask253)
def implement(self):
tt5 = self.getRegistrationImage("tt5", "register")
seed_gmwmi = self.getMaskingImage("tt5", ["register", "5tt2gmwmi"])
norm = self.getRegistrationImage("norm", "resample")
mask253 = self.getMaskingImage('aparc_aseg', ['253', 'mask'])
mask1024= self.getMaskingImage('aparc_aseg', ['1024', 'mask'])
dwi = self.getUpsamplingImage('dwi', 'upsample')
bFile = self.getUpsamplingImage('grad', None, 'b')
mask = self.getRegistrationImage('mask', 'resample')
self.__nbDirections = mriutil.getNbDirectionsFromDWI(dwi)
if self.__nbDirections <= 45:
if 'deterministic' in self.get('algorithm'):
tckDet = self.__tckgenTensor(dwi, self.buildName(dwi, 'tensor_det', 'tck'), mask, tt5, seed_gmwmi, bFile, 'Tensor_Det')
tckDetRoi = self.__tckedit(tckDet, mask253, self.buildName(tckDet, 'roi','tck'))
tckDetRoiTrk = mriutil.tck2trk(tckDetRoi, norm, self.buildName(tckDetRoi, None, 'trk'))
tckDetTrk = mriutil.tck2trk(tckDet, norm, self.buildName(tckDet, None, 'trk'))
self.__tckDetRoiTrk = tckDetRoiTrk
if 'probabilistic' in self.get('algorithm'):
tckProb = self.__tckgenTensor(dwi, self.buildName(dwi, 'tensor_prob', 'tck'), mask, tt5, seed_gmwmi, bFile, 'Tensor_Prob')
tckProbRoi = self.__tckedit(tckProb, mask253, self.buildName(tckProb, 'roi','tck'))
tckProbRoiTrk = mriutil.tck2trk(tckProbRoi, norm , self.buildName(tckProbRoi, None, 'trk'))
tckProbTrk = mriutil.tck2trk(tckProb, norm , self.buildName(tckProb, None, 'trk'))
self.__tckProbRoiTrk = tckProbRoiTrk
else:
if 'hardi' in self.get('algorithm'):
csd = self.getHardimrtrixImage('dwi','csd')
hardiTck = self.__tckgenHardi(csd, self.buildName(csd, 'hardi_prob', 'tck'), tt5)
hardiTckRoi = self.__tckedit(hardiTck, mask253, self.buildName(hardiTck, 'roi','tck'))
tckgenRoiTrk = mriutil.tck2trk(hardiTckRoi, norm , self.buildName(hardiTckRoi, None, 'trk'))
hardiTrk = mriutil.tck2trk(hardiTck, norm, self.buildName(hardiTck, 'None', 'trk'))
self.__tckgenRoiTrk = tckgenRoiTrk
if 'sift' in self.get('algorithm'):
tcksift = self.__tcksift(hardiTck, csd)
tcksiftRoi = self.__tckedit(tcksift, mask253, self.buildName(tcksift, 'roi', 'tck'))
tcksiftRoiTrk = mriutil.tck2trk(tcksiftRoi, norm , self.buildName(tcksiftRoi, None, 'trk'))
tcksiftTrk = mriutil.tck2trk(tcksift, norm, self.buildName(tcksift, 'None', 'trk'))
self.__tcksiftRoiTrk = tcksiftRoiTrk
def implement(self):
tt5 = self.getRegistrationImage("tt5", "register")
seed_gmwmi = self.getMaskingImage("tt5", ["register", "5tt2gmwmi"])
norm = self.getRegistrationImage("norm", "resample")
mask253 = self.getMaskingImage('aparc_aseg', ['253', 'mask'])
# mask1024= self.getMaskingImage('aparc_aseg', ['1024', 'mask'])
dwi = self.getUpsamplingImage('dwi', 'upsample')
bFile = self.getUpsamplingImage('grad', None, 'b')
mask = self.getRegistrationImage('mask', 'resample')
# If step is None set Step = voxelSize/2
if self.get('step') == 'None':
voxelSize = [float(x) for x in self.get('methodology', 't1_voxelsize')[1:-1].split(',')]
self.set('step', str(float(voxelSize[0]) * 0.5))
self.set('angle', str(90 * float(self.get('step')) / float(voxelSize[0])))
self.__nbDirections = mriutil.getNbDirectionsFromDWI(dwi)
if self.__nbDirections <= 45 and not self.get('forceHardi'):
self.set('methodReconstruction', 'tenseur')
if 'deterministic' in self.get('algorithm'):
tckDet = self.__tckgenTensor(
dwi, self.buildName(dwi, 'tensor_det', 'tck'),
mask, tt5, seed_gmwmi, bFile, 'Tensor_Det')
tckDetTrk = mriutil.tck2trk(
tckDet, norm, self.buildName(tckDet, None, 'trk'))
tckDetRoi = self.__tckedit(
tckDet, mask253, self.buildName(tckDet, 'roi', 'tck'))
tckDetRoiTrk = mriutil.tck2trk(
tckDetRoi, norm, self.buildName(tckDetRoi, None, 'trk'))
self.__tckDetRoiTrk = tckDetRoiTrk
self.set('algorithm', 'Determinist') # Set Method tractography Det
elif 'probabilistic' in self.get('algorithm'):
tckProb = self.__tckgenTensor(
dwi, self.buildName(dwi, 'tensor_prob', 'tck'),
mask, tt5, seed_gmwmi, bFile, 'Tensor_Prob')
tckProbTrk = mriutil.tck2trk(
tckProb, norm , self.buildName(tckProb, None, 'trk'))
tckProbRoi = self.__tckedit(
tckProb, mask253, self.buildName(tckProb, 'roi', 'tck'))
tckProbRoiTrk = mriutil.tck2trk(
tckProbRoi, norm , self.buildName(tckProbRoi, None, 'trk'))
self.__tckProbRoiTrk = tckProbRoiTrk
self.set('algorithm', 'Probabilist') # Set Method tractography Prob
else:
csd = self.getHardimrtrixImage('dwi', 'csd')
hardiTck = self.__tckgenHardi(
csd, self.buildName(csd, 'hardi_prob', 'tck'), tt5)
hardiTrk = mriutil.tck2trk(
hardiTck, norm, self.buildName(hardiTck, None, 'trk'))
hardiTckRoi = self.__tckedit(
hardiTck, mask253, self.buildName(hardiTck, 'roi', 'tck'))
tckgenRoiTrk = mriutil.tck2trk(
hardiTckRoi, norm , self.buildName(hardiTckRoi, None, 'trk'))
self.__tckgenRoiTrk = tckgenRoiTrk
self.set('methodReconstruction', 'hardi')
self.set('algorithm', 'Probabilist')
if self.get('sift'):
tcksift = self.__tcksift(hardiTck, csd)
tcksiftTrk = mriutil.tck2trk(
tcksift, norm, self.buildName(tcksift, None, 'trk'))
tcksiftRoi = self.__tckedit(
tcksift, mask253, self.buildName(tcksift, 'roi', 'tck'))
tcksiftRoiTrk = mriutil.tck2trk(
tcksiftRoi, norm , self.buildName(tcksiftRoi, None, 'trk'))
self.__tcksiftRoiTrk = tcksiftRoiTrk
def implement(self):
tt5 = self.getRegistrationImage("tt5", "register")
seed_gmwmi = self.getMaskingImage("tt5", ["register", "5tt2gmwmi"])
norm = self.getRegistrationImage("norm", "resample")
mask253 = self.getMaskingImage('aparc_aseg', ['253', 'mask'])
# mask1024= self.getMaskingImage('aparc_aseg', ['1024', 'mask'])
dwi = self.getUpsamplingImage('dwi', 'upsample')
bFile = self.getUpsamplingImage('grad', None, 'b')
mask = self.getRegistrationImage('mask', 'resample')
# If step is None set Step = voxelSize/2
if self.get('step') == 'None':
voxelSize = [
float(x) for x in self.get('methodology', 't1_voxelsize')
[1:-1].split(',')
]
self.set('step', str(float(voxelSize[0]) * 0.5))
self.set('angle',
str(90 * float(self.get('step')) / float(voxelSize[0])))
self.__nbDirections = mriutil.getNbDirectionsFromDWI(dwi)
if self.__nbDirections <= 45 and not self.get('forceHardi'):
self.set('methodReconstruction', 'tenseur')
if 'deterministic' in self.get('algorithm'):
tckDet = self.__tckgenTensor(
dwi, self.buildName(dwi, 'tensor_det', 'tck'), mask, tt5,
seed_gmwmi, bFile, 'Tensor_Det')
tckDetTrk = mriutil.tck2trk(
tckDet, norm, self.buildName(tckDet, None, 'trk'))
tckDetRoi = self.__tckedit(
tckDet, mask253, self.buildName(tckDet, 'roi', 'tck'))
tckDetRoiTrk = mriutil.tck2trk(
tckDetRoi, norm, self.buildName(tckDetRoi, None, 'trk'))
self.__tckDetRoiTrk = tckDetRoiTrk
self.set('algorithm',
'Determinist') # Set Method tractography Det
elif 'probabilistic' in self.get('algorithm'):
tckProb = self.__tckgenTensor(
dwi, self.buildName(dwi, 'tensor_prob', 'tck'), mask, tt5,
seed_gmwmi, bFile, 'Tensor_Prob')
tckProbTrk = mriutil.tck2trk(
tckProb, norm, self.buildName(tckProb, None, 'trk'))
tckProbRoi = self.__tckedit(
tckProb, mask253, self.buildName(tckProb, 'roi', 'tck'))
tckProbRoiTrk = mriutil.tck2trk(
tckProbRoi, norm, self.buildName(tckProbRoi, None, 'trk'))
self.__tckProbRoiTrk = tckProbRoiTrk
self.set('algorithm',
'Probabilist') # Set Method tractography Prob
else:
csd = self.getHardimrtrixImage('dwi', 'csd')
hardiTck = self.__tckgenHardi(
csd, self.buildName(csd, 'hardi_prob', 'tck'), tt5)
hardiTrk = mriutil.tck2trk(hardiTck, norm,
self.buildName(hardiTck, None, 'trk'))
hardiTckRoi = self.__tckedit(
hardiTck, mask253, self.buildName(hardiTck, 'roi', 'tck'))
tckgenRoiTrk = mriutil.tck2trk(
hardiTckRoi, norm, self.buildName(hardiTckRoi, None, 'trk'))
self.__tckgenRoiTrk = tckgenRoiTrk
self.set('methodReconstruction', 'hardi')
self.set('algorithm', 'Probabilist')
if self.get('sift'):
tcksift = self.__tcksift(hardiTck, csd)
tcksiftTrk = mriutil.tck2trk(
tcksift, norm, self.buildName(tcksift, None, 'trk'))
tcksiftRoi = self.__tckedit(
tcksift, mask253, self.buildName(tcksift, 'roi', 'tck'))
tcksiftRoiTrk = mriutil.tck2trk(
tcksiftRoi, norm, self.buildName(tcksiftRoi, None, 'trk'))
self.__tcksiftRoiTrk = tcksiftRoiTrk
