def implement(self):
#Noise mask computation
brainMask = self.getCorrectionImage('mask', 'corrected')
noiseMask = mriutil.computeNoiseMask(brainMask, self.buildName(brainMask, 'noisemask'))
#Voxel Size of native dwi
dwiNative = self.getPreparationImage('dwi')
voxelSize = mriutil.getMriVoxelSize(dwiNative)
#Corpus Callosum masks
ccMask = self.getMaskingImage('aparc_aseg', ['253','mask'])
ccMaskDownsample = self.buildName(ccMask, 'downsample')
cmdString = "mri_convert -voxsize {} -rl {} --input_volume {} --output_volume {}"
cmd = cmdString.format(" ".join(voxelSize), brainMask, ccMask, ccMaskDownsample)
self.launchCommand(cmd)
def implement(self):
# Noise mask computation
brainMask = self.getCorrectionImage("mask", "corrected")
noiseMask = mriutil.computeNoiseMask(brainMask, self.buildName(brainMask, "noisemask"))
# Voxel Size of native dwi
dwiNative = self.getPreparationImage("dwi")
voxelSize = mriutil.getMriVoxelSize(dwiNative)
# Corpus Callosum masks
ccMask = self.getMaskingImage("aparc_aseg", ["253", "mask"])
ccMaskDownsample = self.buildName(ccMask, "downsample")
cmdString = "mri_convert -voxsize {} -rl {} --input_volume {} --output_volume {}"
cmd = cmdString.format(" ".join(voxelSize), brainMask, ccMask, ccMaskDownsample)
self.launchCommand(cmd)
def implement(self):
#Noise mask computation
brainMask = self.getCorrectionImage('mask', 'corrected')
noiseMask = mriutil.computeNoiseMask(
brainMask, self.buildName(brainMask, 'noisemask'))
#Voxel Size of native dwi
dwiNative = self.getPreparationImage('dwi')
voxelSize = mriutil.getMriVoxelSize(dwiNative)
#Corpus Callosum masks
ccMask = self.getMaskingImage('aparc_aseg', ['253', 'mask'])
ccMaskDownsample = self.buildName(ccMask, 'downsample')
cmdString = "mri_convert -voxsize {} -rl {} --input_volume {} --output_volume {}"
cmd = cmdString.format(" ".join(voxelSize), brainMask, ccMask,
ccMaskDownsample)
self.launchCommand(cmd)
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 = ""
if self.get("parcellation", "intrasubject"):
extraArgs += " -usesqform -dof 6"
mask = mriutil.computeDwiMaskFromFreesurfer(b0,
norm,
parcellationMask,
self.buildName(parcellationMask, 'resample'),
extraArgs)
target = self.buildName(dwi, "denoise")
if 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")))
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")))
