def implement(self):
dwi = self.__getDwiImage()
bVals = self.__getBValsImage()
norm= self.getParcellationImage('norm')
parcellationMask = self.getParcellationImage('mask')
b0 = os.path.join(self.workingDir, os.path.basename(dwi).replace(self.get("prefix", 'dwi'), self.get("prefix", 'b0')))
self.info(mriutil.extractFirstB0FromDwi(dwi, b0, bVals))
self.info("create a suitable mask for the dwi")
extraArgs = ""
if self.get("parcellation", "intrasubject"):
extraArgs += " -usesqform -dof 6"
mask = mriutil.computeDwiMaskFromFreesurfer(b0,
norm,
parcellationMask,
self.buildName(parcellationMask, 'temporary'),
extraArgs)
target = self.buildName(dwi, "denoise")
if self.get("algorithm") == "nlmeans":
dwiImage = nibabel.load(dwi)
dwiData = dwiImage.get_data()
self.sigmaVector, sigma, maskNoise = self.__computeSigmaAndNoiseMask(dwiData)
self.info("sigma value that will be apply into nlmeans = {}".format(sigma))
denoisingData = dipy.denoise.nlmeans.nlmeans(dwiData, sigma)
nibabel.save(nibabel.Nifti1Image(denoisingData.astype(numpy.float32), dwiImage.get_affine()), target)
nibabel.save(nibabel.Nifti1Image(maskNoise.astype(numpy.float32),
dwiImage.get_affine()), self.buildName(target, "noise_mask"))
elif self.get('general', 'matlab_available'):
dwi = self.__getDwiImage()
dwiUncompress = self.uncompressImage(dwi)
tmp = self.buildName(dwiUncompress, "tmp", 'nii')
scriptName = self.__createMatlabScript(dwiUncompress, tmp)
self.__launchMatlabExecution(scriptName)
self.info("compressing {} image".format(tmp))
tmpCompress = util.gzip(tmp)
self.rename(tmpCompress, target)
if self.get("cleanup"):
self.info("Removing redundant image {}".format(dwiUncompress))
os.remove(dwiUncompress)
else:
self.matlabWarning = True
self.warning("Algorithm {} is set but matlab is not available for this server.\n"
"Please configure matlab or set denoising algorithm to nlmeans or none"
.format(self.get("algorithm")))
def __computeFieldmap(self, dwi, bVals, mag, phase, norm, parcellationMask, freesurferAnat):
# extract a b0 from the dwi image
b0 = os.path.join(self.workingDir, os.path.basename(dwi).replace(self.get("prefix", 'dwi'), "b0_fieldmap_tmp"))
self.info(mriutil.extractFirstB0FromDwi(dwi, b0, bVals))
self.info("rescaling the phase image")
phaseRescale = self.__rescaleFieldMap(phase)
self.info('Coregistring magnitude image with the anatomical image produce by freesurfer')
fieldmapToAnat = self.__coregisterFieldmapToAnat(mag, freesurferAnat)
extraArgs = " -dof 6 "
if self.get("methodology", "intrasession"):
extraArgs += " -usesqform -dof 6"
interpolateMask, fm2anat, anat2fm = mriutil.computeDwiMaskFromFreesurfer(
mag,
norm,
parcellationMask,
self.buildName(parcellationMask, 'interpolate'),
extraArgs)
self.info('Resampling the anatomical mask into the phase image space')
# interpolateMask = self.__interpolateAnatMaskToFieldmap(anat, phaseRescale, invertFielmapToAnat, mask)
fieldmap = self.__computePhaseFieldmap(phaseRescale, interpolateMask)
self.info('Generate a lossy magnitude file with signal loss and distortion')
lossy = self.__simulateLossyMap(fieldmap, interpolateMask)
magnitudeMask = self.__computeMap(mag, interpolateMask, 'brain')
lossyMagnitude = self.__computeMap(magnitudeMask, lossy, 'lossy')
warped = self.__computeForwardDistorsion(fieldmap, lossyMagnitude, interpolateMask)
self.info('Coregister the simulated lossy fieldmap with the EPI')
matrixName = self.get("epiTo_b0fm")
self.__coregisterEpiLossyMap(b0, warped, matrixName, lossy)
self.info('Reslice magnitude and fieldmap in the EPI space')
invertMatrixName = self.buildName(matrixName, 'inverse', 'mat')
self.info(mriutil.invertMatrix(matrixName, invertMatrixName))
magnitudeIntoDwiSpace = self.__interpolateFieldmapInEpiSpace(warped, b0, invertMatrixName)
magnitudeIntoDwiSpaceMask = self.__mask(parcellationMask, b0, anat2fm, invertMatrixName)
interpolateFieldmap = self.__interpolateFieldmapInEpiSpace(fieldmap, b0, invertMatrixName)
self.info('Create the shift map')
saveshift = self.__performDistortionCorrection(b0, interpolateFieldmap, magnitudeIntoDwiSpaceMask)
self.info('Perform distortion correction of EPI data')
dwiUnwarp = self.__performDistortionCorrectionToDWI(dwi, magnitudeIntoDwiSpaceMask, saveshift)
return dwiUnwarp
def __computeFieldmap(self, dwi, bVals, mag, phase, norm, parcellationMask,
freesurferAnat):
# extract a b0 from the dwi image
b0 = os.path.join(
self.workingDir,
os.path.basename(dwi).replace(self.get("prefix", 'dwi'),
"b0_fieldmap_tmp"))
self.info(mriutil.extractFirstB0FromDwi(dwi, b0, bVals))
self.info("rescaling the phase image")
phaseRescale = self.__rescaleFieldMap(phase)
self.info(
'Coregistring magnitude image with the anatomical image produce by freesurfer'
)
fieldmapToAnat = self.__coregisterFieldmapToAnat(mag, freesurferAnat)
extraArgs = " -dof 6 "
if self.get("methodology", "intrasession"):
extraArgs += " -usesqform -dof 6"
interpolateMask = mriutil.computeDwiMaskFromFreesurfer(
mag, norm, parcellationMask,
self.buildName(parcellationMask, 'interpolate'), extraArgs)
self.info('Resampling the anatomical mask into the phase image space')
# interpolateMask = self.__interpolateAnatMaskToFieldmap(anat, phaseRescale, invertFielmapToAnat, mask)
fieldmap = self.__computePhaseFieldmap(phaseRescale, interpolateMask)
self.info(
'Generate a lossy magnitude file with signal loss and distortion')
lossy = self.__simulateLossyMap(fieldmap, interpolateMask)
magnitudeMask = self.__computeMap(mag, interpolateMask, 'brain')
lossyMagnitude = self.__computeMap(magnitudeMask, lossy, 'lossy')
warped = self.__computeForwardDistorsion(fieldmap, lossyMagnitude,
interpolateMask)
self.info('Coregister the simulated lossy fieldmap with the EPI')
matrixName = self.get("epiTo_b0fm")
self.__coregisterEpiLossyMap(b0, warped, matrixName, lossy)
self.info('Reslice magnitude and fieldmap in the EPI space')
invertMatrixName = self.buildName(matrixName, 'inverse', 'mat')
self.info(mriutil.invertMatrix(matrixName, invertMatrixName))
magnitudeIntoDwiSpace = self.__interpolateFieldmapInEpiSpace(
warped, b0, invertMatrixName)
magnitudeIntoDwiSpaceMask = self.__mask(magnitudeIntoDwiSpace)
interpolateFieldmap = self.__interpolateFieldmapInEpiSpace(
fieldmap, b0, invertMatrixName)
self.info('Create the shift map')
saveshift = self.__performDistortionCorrection(
b0, interpolateFieldmap, magnitudeIntoDwiSpaceMask)
self.info('Perform distortion correction of EPI data')
dwiUnwarp = self.__performDistortionCorrectionToDWI(
dwi, magnitudeIntoDwiSpaceMask, saveshift)
return dwiUnwarp
def implement(self):
dwi = self.getDenoisingImage('dwi', 'denoise')
if not dwi:
dwi = self.getPreparationImage('dwi')
b0AP = self.getPreparationImage('b0_ap')
b0PA = self.getPreparationImage('b0_pa')
bEnc = self.getPreparationImage('grad', None, 'b')
bVals = self.getPreparationImage('grad', None, 'bvals')
bVecs = self.getPreparationImage('grad', None, 'bvecs')
norm = self.getParcellationImage('norm')
parcellationMask = self.getParcellationImage('mask')
# Fieldmap only
mag = self.getPreparationImage("mag")
phase = self.getPreparationImage("phase")
freesurferAnat = self.getParcellationImage('anat', 'freesurfer')
self.info("extract b0 image from the dwi")
b0 = os.path.join(
self.workingDir,
os.path.basename(dwi).replace(self.get("prefix", 'dwi'),
self.get("prefix", 'b0')))
self.info(mriutil.extractFirstB0FromDwi(dwi, b0, bVals))
self.info(
"look if all images have the same voxel size and dimension scale")
self.__validateSizeAndDimension(dwi, b0, b0AP, b0PA)
# Generate a missing b0 image if we could. --> 0 = P>>A, 1 = A>>P
if self.get("phase_enc_dir") == "0" and b0AP and b0PA is False:
b0PA = b0
if self.get("phase_enc_dir") == "1" and b0PA and b0AP is False:
b0AP = b0
[dwi, b0, b0AP,
b0PA] = self.__oddEvenNumberOfSlices(dwi, b0, b0AP, b0PA)
self.set('method', None)
if b0AP is False or b0PA is False:
topupBaseName = None
b0Image = b0
self.set('method', 'fieldmap')
else:
# Concatenate B0 image together
if self.get("phase_enc_dir") == "0":
concatenateB0Image = self.__concatenateB0(
b0PA, b0AP, self.buildName("b0pa_b0ap", None, "nii.gz"))
elif self.get("phase_enc_dir") == "1":
concatenateB0Image = self.__concatenateB0(
b0AP, b0PA, self.buildName("b0ap_b0pa", None, "nii.gz"))
# Create the acquisition parameter file
acqpTopup = self.__createAcquisitionParameterFile('topup')
# Run topup on concatenate B0 image
[topupBaseName,
topupImage] = self.__topup(concatenateB0Image, acqpTopup,
self.get('b02b0_filename'))
b0Image = self.__fslmathsTmean(
os.path.join(self.workingDir, topupImage))
self.set('method', 'topup')
self.info("create a suitable mask for the dwi")
extraArgs = " -dof 6 " # same subject
if self.get("methodology", "intrasession"):
extraArgs += " -usesqform "
mask = mriutil.computeDwiMaskFromFreesurfer(
b0Image, norm, parcellationMask,
self.buildName(parcellationMask, 'temporary'), extraArgs)
# Create the acquisition parameter file for eddy
acqpEddy = self.__createAcquisitionParameterFile('eddy')
# Create an index file
indexFile = self.__createIndexFile(mriutil.getNbDirectionsFromDWI(dwi))
outputImage = self.__correctionEddy(dwi, mask, topupBaseName,
indexFile, acqpEddy, bVecs, bVals)
eddyParameterFiles = self.getImage('dwi', None, 'eddy_parameters')
if eddyParameterFiles:
self.info(
"Apply eddy movement correction to gradient encodings directions"
)
bEnc = mriutil.applyGradientCorrection(
bEnc, eddyParameterFiles,
self.buildName(outputImage, None, 'b'))
self.info(
mriutil.mrtrixToFslEncoding(
outputImage, bEnc,
self.buildName(outputImage, None, 'bvecs'),
self.buildName(outputImage, None, 'bvals')))
# Proceed with fieldmap if provided
if mag and phase and not self.__topupCorrection:
# OutputImage is now used for fieldmap correction
outputImage = self.__computeFieldmap(outputImage, bVals, mag,
phase, norm, parcellationMask,
freesurferAnat)
self.set('method', 'fieldmap')
# Produce a valid b0 and mask for QA
b0Corrected = self.buildName(b0, 'corrected')
self.info(
mriutil.extractFirstB0FromDwi(outputImage, b0Corrected, bVals))
maskCorrected = mriutil.computeDwiMaskFromFreesurfer(
b0Corrected, norm, parcellationMask,
self.buildName(parcellationMask, 'corrected'), extraArgs)
self.rename(outputImage, self.buildName(outputImage, 'corrected'))
def implement(self):
dwi = self.getDenoisingImage('dwi', 'denoise')
if not dwi:
dwi = self.getPreparationImage('dwi')
b0AP = self.getPreparationImage('b0_ap')
b0PA = self.getPreparationImage('b0_pa')
bEnc = self.getPreparationImage('grad', None, 'b')
bVals = self.getPreparationImage('grad', None, 'bvals')
bVecs = self.getPreparationImage('grad', None, 'bvecs')
norm = self.getParcellationImage('norm')
parcellationMask = self.getParcellationImage('mask')
# Fieldmap only
mag = self.getPreparationImage("mag")
phase = self.getPreparationImage("phase")
freesurferAnat = self.getParcellationImage('anat', 'freesurfer')
self.info("extract b0 image from the dwi")
b0 = os.path.join(self.workingDir,
os.path.basename(dwi).replace(self.get("prefix", 'dwi'), self.get("prefix", 'b0')))
self.info(mriutil.extractFirstB0FromDwi(dwi, b0, bVals))
self.info("look if all images have the same voxel size and dimension scale")
self.__validateSizeAndDimension(dwi, b0, b0AP, b0PA)
# Generate a missing b0 image if we could. --> 0 = P>>A, 1 = A>>P
if self.get("phase_enc_dir") == "0" and b0AP and b0PA is False:
b0PA = b0
if self.get("phase_enc_dir") == "1" and b0PA and b0AP is False:
b0AP = b0
[dwi, b0, b0AP, b0PA] = self.__oddEvenNumberOfSlices(dwi, b0, b0AP, b0PA)
self.set('method', None)
if b0AP is False or b0PA is False:
topupBaseName = None
b0Image = b0
self.set('method', 'fieldmap')
else:
# Concatenate B0 image together
if self.get("phase_enc_dir") == "0":
concatenateB0Image = self.__concatenateB0(b0PA, b0AP, self.buildName("b0pa_b0ap", None, "nii.gz"))
elif self.get("phase_enc_dir") == "1":
concatenateB0Image = self.__concatenateB0(b0AP, b0PA, self.buildName("b0ap_b0pa", None, "nii.gz"))
# Create the acquisition parameter file
acqpTopup = self.__createAcquisitionParameterFile('topup')
# Run topup on concatenate B0 image
[topupBaseName, topupImage] = self.__topup(concatenateB0Image, acqpTopup, self.get('b02b0_filename'))
b0Image = self.__fslmathsTmean(os.path.join(self.workingDir, topupImage))
self.set('method', 'topup')
self.info("create a suitable mask for the dwi")
extraArgs = " -dof 6 " # same subject
if self.get("methodology", "intrasession"):
extraArgs += " -usesqform "
mask = mriutil.computeDwiMaskFromFreesurfer(b0Image,
norm,
parcellationMask,
self.buildName(parcellationMask, 'temporary'),
extraArgs)
# Create the acquisition parameter file for eddy
acqpEddy = self.__createAcquisitionParameterFile('eddy')
# Create an index file
indexFile = self.__createIndexFile(mriutil.getNbDirectionsFromDWI(dwi))
outputImage = self.__correctionEddy(dwi, mask, topupBaseName, indexFile, acqpEddy, bVecs, bVals)
eddyParameterFiles = self.getImage('dwi', None, 'eddy_parameters')
if eddyParameterFiles:
self.info("Apply eddy movement correction to gradient encodings directions")
bEnc = mriutil.applyGradientCorrection(bEnc, eddyParameterFiles, self.buildName(outputImage, None, 'b'))
self.info(mriutil.mrtrixToFslEncoding(outputImage,
bEnc,
self.buildName(outputImage, None, 'bvecs'),
self.buildName(outputImage, None, 'bvals')))
# Proceed with fieldmap if provided
if mag and phase and not self.__topupCorrection:
# OutputImage is now used for fieldmap correction
outputImage = self.__computeFieldmap(outputImage, bVals, mag, phase, norm, parcellationMask, freesurferAnat)
self.set('method', 'fieldmap')
# Produce a valid b0 and mask for QA
b0Corrected = self.buildName(b0, 'corrected')
self.info(mriutil.extractFirstB0FromDwi(outputImage, b0Corrected, bVals))
maskCorrected = mriutil.computeDwiMaskFromFreesurfer(b0Corrected,
norm,
parcellationMask,
self.buildName(parcellationMask, 'corrected'),
extraArgs)
self.rename(outputImage, self.buildName(outputImage, 'corrected'))
def implement(self):
dwi = self.getPreparationImage("dwi")
bVals = self.getPreparationImage('grad', None, 'bvals')
norm= self.getParcellationImage('norm')
parcellationMask = self.getParcellationImage('mask')
b0 = os.path.join(self.workingDir, os.path.basename(dwi).replace(self.get("prefix", 'dwi'), self.get("prefix", 'b0')))
self.info(mriutil.extractFirstB0FromDwi(dwi, b0, bVals))
self.info("create a suitable mask for the dwi")
extraArgs = " -dof 6 "
if self.get("methodology", "intrasession"):
extraArgs += " -usesqform "
mask = mriutil.computeDwiMaskFromFreesurfer(b0,
norm,
parcellationMask,
self.buildName(parcellationMask, 'resample'),
extraArgs)
target = self.buildName(dwi, "denoise")
if self.get("algorithm") == "mp-pca":
targetNoise = self.buildName(dwi, "noise")
self.algorithm = "mp-pca"
#cmd = "dwidenoise {} {} -mask {} -noise {} -extent {} -nthreads {} -quiet".format(dwi, target, mask, targetNoise, self.get('extent'), self.getNTreadsMrtrix())
cmd = "dwidenoise {} {} -noise {} -extent {} -nthreads {} -quiet".format(dwi, target, targetNoise, self.get('extent'), self.getNTreadsMrtrix())
self.launchCommand(cmd)
elif self.get("algorithm") == "nlmeans":
self.algorithm = "nlmeans"
dwiImage = nibabel.load(dwi)
dwiData = dwiImage.get_data()
#if self.get('number_array_coil') == "32":
noiseMask = mriutil.computeNoiseMask(mask, self.buildName(mask, 'noise_mask'))
noiseMaskImage = nibabel.load(noiseMask)
noiseMaskData = noiseMaskImage.get_data()
sigma = numpy.std(dwiData[noiseMaskData > 0])
self.info("sigma value that will be apply into nlmeans = {}".format(sigma))
denoisingData = dipy.denoise.nlmeans.nlmeans(dwiData, sigma)
#else:
#self.sigmaVector, sigma, piesnoNoiseMask = self.__computeSigmaAndNoiseMask(dwiData)
#self.info("sigma value that will be apply into nlmeans = {}".format(sigma))
#denoisingData = dipy.denoise.nlmeans.nlmeans(dwiData, sigma)
#nibabel.save(nibabel.Nifti1Image(piesnoNoiseMask.astype(numpy.float32),dwiImage.get_affine()), self.buildName(target, "piesno_noise_mask"))
nibabel.save(nibabel.Nifti1Image(denoisingData.astype(numpy.float32), dwiImage.get_affine()), target)
elif self.get('general', 'matlab_available'):
dwiUncompress = self.uncompressImage(dwi)
tmp = self.buildName(dwiUncompress, "tmp", 'nii')
scriptName = self.__createMatlabScript(dwiUncompress, tmp)
self.__launchMatlabExecution(scriptName)
self.info("compressing {} image".format(tmp))
tmpCompress = util.gzip(tmp)
self.rename(tmpCompress, target)
if self.get("cleanup"):
self.info("Removing redundant image {}".format(dwiUncompress))
os.remove(dwiUncompress)
else:
self.matlabWarning = True
self.warning("Algorithm {} is set but matlab is not available for this server.\n"
"Please configure matlab or set denoising algorithm to nlmeans or none"
.format(self.get("algorithm")))