def qaSupplier(self):
"""Create and supply images for the report generated by qa task
"""
qaImages = Images()
softwareName = 'mrtrix'
#Set information
information = "Estimation using WLS with {} iteration(s)".format(self.get('iter'))
qaImages.setInformation(information)
#Get images
mask = self.getRegistrationImage('mask', 'resample')
#Build qa images
tags = (
('fa', 0.7, 'Fractional anisotropy'),
('ad', 0.005, 'Axial Diffusivity'),
('md', 0.005, 'Mean Diffusivity'),
('rd', 0.005, 'Radial Diffusivity'),
)
for postfix, vmax, description in tags:
image = self.getImage('dwi', postfix)
if image:
imageQa = self.plot3dVolume(
image, fov=mask, vmax=vmax,
colorbar=True, postfix=softwareName)
qaImages.append((imageQa, description))
return qaImages
def qaSupplier(self):
"""Create and supply images for the report generated by qa task
"""
qaImages = Images()
information = "Warning: due to storage restriction, streamlines were " \
"downsampled. Even if there is no difference in structural " \
"connectivity, you should be careful before computing any " \
"metrics along these streamlines.\n To run toad without this " \
"downsampling, please refer to the documentation."
if self.defaultQuery:
# get images
norm = self.getRegistrationImage("norm", "resample")
self.__buildNameTractQuerierOutputs()
# images production
tags = (
(self.queries[0],
'Corpus Callosum',
95, 60, 40, -80, 0, 160),
(self.queries[1],
'Inferior Fronto Occipital tract left',
95, 80, 40, -90, 0, 90),
(self.queries[2],
'Inferior Fronto Occipital tract right',
95, 80, 40, -90, 0, -90),
(self.queries[3],
'inferior Longitudinal Fasciculus left',
95, 80, 40, -90, 0, 90),
(self.queries[4],
'Inferior Longitudinal Fasciculus right',
95, 80, 40, -90, 0, -90),
(self.queries[5],
'Uncinate Fasciculus left',
95, 80, 40, -90, 0, 90),
(self.queries[6],
'Uncinate Fasciculus right',
95, 80, 40, -90, 0, -90),
(self.queries[7],
'Corticospinal tract Left',
95, 80, 40, -90, 0, 160),
(self.queries[8],
'Corticospinal tract right',
95, 80, 40, -90, 0, 200),
)
for data, description, xSlice, ySlice, zSlice, xRot, yRot, zRot in tags:
if data:
imageQa = self.plotTrk(data, norm, None, xSlice, ySlice, zSlice, xRot, yRot, zRot)
qaImages.append((imageQa, description))
else:
information = """
Because you didn't choose default queries and dictionnary,
we are not able to create proper screenshots of the output bundles.
"""
qaImages.setInformation(information)
return qaImages
def qaSupplier(self):
"""Create and supply images for the report generated by qa task
"""
qaImages = Images()
#Information on denoising algorithm
information = 'Denoising was done using the {} algorithm'.format(self.algorithm)
if self.algorithm == "nlmeans" and \
self.config.get("denoising", "number_array_coil") == "32":
information = "NLMEANS algorithm is not yet implemented for 32 " \
"coils channels images, "
if self.config.getboolean("general", "matlab_available"):
information += "set algorithm to `lpca` or `aonlm` or "
information += "set `ignore: True` into the [denoising] section " \
"of your config.cfg file."
if self.matlabWarning:
information = "Algorithm `aonlm` or `lpca` was set for the " \
"denoising, but Matlab is not available for this server. "\
"Please install and configure Matlab or set `ignore: True`"\
" into the [denoising] section of your config.cfg file."
qaImages.extend(Images((False, 'Denoised diffusion image')))
qaImages.setInformation(information)
#Get images
dwi = self.getPreparationImage("dwi")
dwiDenoised = self.getImage('dwi', 'denoise')
brainMask = self.getImage('mask', 'resample')
b0 = self.getImage('b0')
noiseMask = self.getImage('dwi', 'noise_mask')
#Build qa images
if dwiDenoised:
dwiDenoisedQa = self.plot4dVolume(dwiDenoised, fov=brainMask)
qaImages.append((dwiDenoisedQa, 'Denoised diffusion image'))
dwiCompareQa = self.compare4dVolumes(
dwi, dwiDenoised, fov=brainMask)
qaImages.append((dwiCompareQa, 'Before and after denoising'))
if self.algorithm == "nlmeans":
if self.sigmaVector != None:
sigmaQa = self.plotSigma(self.sigmaVector, dwiDenoised)
qaImages.append(
(sigmaQa, 'Sigmas from the nlmeans algorithm'))
if noiseMask:
noiseMaskQa = self.plot3dVolume(
b0, edges=noiseMask, fov=noiseMask)
qaImages.append(
(noiseMaskQa, 'Noise mask from the nlmeans algorithm'))
return qaImages
def qaSupplier(self):
"""Create and supply images for the report generated by qa task
"""
qaImages = Images()
information = "Warning: due to storage restriction, streamlines were " \
"downsampled. Even if there is no difference in structural " \
"connectivity, you should be careful before computing any " \
"metrics along these streamlines.\n To run toad without this " \
"downsampling, please refer to the documentation."
if self.defaultQuery:
# get images
norm = self.getRegistrationImage("norm", "resample")
self.__buildNameTractfilteringOutputs()
# images production
tags = (
(self.outputs[0], 'Corpus Callosum', 95, 60, 40, -80, 0, 160),
(self.outputs[1], 'Inferior Fronto Occipital tract left', 95,
80, 40, -90, 0, 90),
(self.outputs[2], 'Inferior Fronto Occipital tract right', 95,
80, 40, -90, 0, -90),
(self.outputs[3], 'inferior Longitudinal Fasciculus left', 95,
80, 40, -90, 0, 90),
(self.outputs[4], 'Inferior Longitudinal Fasciculus right', 95,
80, 40, -90, 0, -90),
(self.outputs[5], 'Uncinate Fasciculus left', 95, 80, 40, -90,
0, 90),
(self.outputs[6], 'Uncinate Fasciculus right', 95, 80, 40, -90,
0, -90),
(self.outputs[7], 'Corticospinal tract Left', 95, 80, 40, -90,
0, 160),
(self.outputs[8], 'Corticospinal tract right', 95, 80, 40, -90,
0, 200),
)
for data, description, xSlice, ySlice, zSlice, xRot, yRot, zRot in tags:
if data is not None:
imageQa = self.plotTrk(data, norm, None, xSlice, ySlice,
zSlice, xRot, yRot, zRot)
qaImages.append((imageQa, description))
else:
# Add message about QA
pass
else:
information = """
Because you didn't choose default queries and dictionnary,
we are not able to create proper screenshots of the output bundles.
"""
qaImages.setInformation(information)
return qaImages
def qaSupplier(self):
"""Create and supply images for the report generated by qa task
"""
qaImages = Images()
#Information on distorsion correction
b0_ap = self.getPreparationImage('b0_ap')
b0_pa = self.getPreparationImage('b0_pa')
mag = self.getPreparationImage('mag')
phase = self.getPreparationImage('phase')
information = ''
if b0_ap and b0_pa:
information = 'Distortion correction done with AP and PA images'
elif mag and phase:
information = 'Distorsion correction done with fieldmap images'
else:
information = 'No distortion correction done'
qaImages.setInformation(information)
#Get images
dwi = self.getPreparationImage('dwi')
dwiCorrected = self.getImage('dwi', 'corrected')
brainMask = self.getImage('mask', 'corrected')
eddyParameterFiles = self.getImage('dwi', None, 'eddy_parameters')
bVecs= self.getPreparationImage('grad', None, 'bvecs')
bVecsCorrected = self.getImage('grad', None, 'bvecs')
#Build qa names
dwiCorrectedGif = self.buildName(dwiCorrected, None, 'gif')
dwiCompareGif = self.buildName(dwiCorrected, 'compare', 'gif')
translationsPng = self.buildName(dwiCorrected, 'translations', 'png')
rotationPng = self.buildName(dwiCorrected, 'rotations', 'png')
bVecsGif = self.buildName(dwiCorrected, 'vectors', 'gif')
#Build qa images
self.slicerGif(dwiCorrected, dwiCorrectedGif, boundaries=brainMask)
self.slicerGifCompare(dwi, dwiCorrected, dwiCompareGif, boundaries=brainMask)
self.plotMovement(eddyParameterFiles, translationsPng, rotationPng)
self.plotvectors(bVecs, bVecsCorrected, bVecsGif)
qaImages.extend(Images(
(dwiCorrectedGif, 'DWI corrected'),
(dwiCompareGif, 'Before and after corrections'),
(translationsPng, 'Translation correction by eddy'),
(rotationPng, 'Rotation correction by eddy'),
(bVecsGif, 'Gradients vectors on the unitary sphere. Red : raw bvec | Blue : opposite bvec | Black + : movement corrected bvec'),
))
return qaImages
def qaSupplier(self):
"""Create and supply images for the report generated by qa task
"""
qaImages = Images()
#Information on denoising algorithm
information = 'Denoising was done using the {} algorithm'.format(self.algorithm)
if self.matlabWarning:
information = "Algorithm `aonlm` or `lpca` was set for the " \
"denoising, but Matlab is not available for this server. "\
"Please install and configure Matlab or set `ignore: True`"\
" into the [denoising] section of your config.cfg file."
qaImages.extend(Images((False, 'Denoised diffusion image')))
qaImages.setInformation(information)
#Get images
dwi = self.getPreparationImage("dwi")
dwiDenoised = self.getImage('dwi', 'denoise')
brainMask = self.getImage('mask', 'resample')
b0 = self.getImage('b0')
noiseMask = self.getImage('dwi', 'noise_mask')
#Build qa images
if dwiDenoised:
dwiDenoisedQa = self.plot4dVolume(dwiDenoised, fov=brainMask)
qaImages.append((dwiDenoisedQa, 'Denoised diffusion image'))
dwiCompareQa = self.compare4dVolumes(
dwi, dwiDenoised, fov=brainMask)
qaImages.append((dwiCompareQa, 'Before and after denoising'))
if self.algorithm == "nlmeans":
if self.sigmaVector != None:
sigmaQa = self.plotSigma(self.sigmaVector, dwiDenoised)
qaImages.append(
(sigmaQa, 'Sigmas from the nlmeans algorithm'))
if noiseMask:
noiseMaskQa = self.plot3dVolume(
b0, edges=noiseMask, fov=noiseMask)
qaImages.append(
(noiseMaskQa, 'Noise mask from the nlmeans algorithm'))
return qaImages
def qaSupplier(self):
"""Create and supply images for the report generated by qa task
"""
# Get images
dwi = self.getDenoisingImage('dwi', 'denoise')
if not dwi:
dwi = self.getPreparationImage('dwi')
dwiCorrected = self.getImage('dwi', 'corrected')
brainMask = self.getImage('mask', 'corrected')
eddyParameterFiles = self.getImage('dwi', None, 'eddy_parameters')
bVecs = self.getPreparationImage('grad', None, 'bvecs')
bVecsCorrected = self.getImage('grad', None, 'bvecs')
# Build qa images
dwiCorrectedQa = self.plot4dVolume(dwiCorrected, fov=brainMask)
dwiCompareQa = self.compare4dVolumes(dwi, dwiCorrected, fov=brainMask)
translationsQa, rotationsQa = self.plotMovement(
eddyParameterFiles, dwiCorrected)
bVecsQa = self.plotVectors(bVecs, bVecsCorrected, dwiCorrected)
qaImages = Images(
(dwiCorrectedQa, 'DWI corrected'),
(dwiCompareQa, 'Before and after corrections'),
(translationsQa, 'Translation corrections by Eddy'),
(rotationsQa, 'Rotation corrections by Eddy'),
(bVecsQa,
"Gradients vectors on the unitary sphere. " \
"Red: raw bvec | Blue: opposite bvec | " \
"Black +: movement corrected bvec. The more corrected, " \
"the more the + is from the center of the circle."))
# Information on distorsion correction
information = "Eddy movement corrections were applied to the images "
correctionMethod = self.get('method')
if correctionMethod == 'topup':
information += "and distortion corrections were conducted on the " \
"AP and PA images."
elif correctionMethod == 'fieldmap':
information += "using the fieldmap images."
else:
information += "with no distortion correction"
qaImages.setInformation(information)
return qaImages
def qaSupplier(self):
"""Create and supply images for the report generated by qa task
"""
# Get images
dwi = self.getDenoisingImage('dwi', 'denoise')
if not dwi:
dwi = self.getPreparationImage('dwi')
dwiCorrected = self.getImage('dwi', 'corrected')
brainMask = self.getImage('mask', 'corrected')
eddyParameterFiles = self.getImage('dwi', None, 'eddy_parameters')
bVecs = self.getPreparationImage('grad', None, 'bvecs')
bVecsCorrected = self.getImage('grad', None, 'bvecs')
# Build qa images
dwiCorrectedQa = self.plot4dVolume(dwiCorrected, fov=brainMask)
dwiCompareQa = self.compare4dVolumes(dwi, dwiCorrected, fov=brainMask)
translationsQa, rotationsQa = self.plotMovement(
eddyParameterFiles, dwiCorrected)
bVecsQa = self.plotVectors(bVecs, bVecsCorrected, dwiCorrected)
qaImages = Images(
(dwiCorrectedQa, 'DWI corrected'),
(dwiCompareQa, 'Before and after corrections'),
(translationsQa, 'Translation corrections by Eddy'),
(rotationsQa, 'Rotation corrections by Eddy'),
(bVecsQa,
"Gradients vectors on the unitary sphere. " \
"Red: raw bvec | Blue: opposite bvec | " \
"Black +: movement corrected bvec. The more corrected, " \
"the more the + is from the center of the circle."))
# Information on distorsion correction
information = "Eddy movement corrections were applied to the images "
correctionMethod = self.get('method')
if correctionMethod == 'topup':
information += "and distortion corrections were conducted on the " \
"AP and PA images."
elif correctionMethod == 'fieldmap':
information += "using the fieldmap images."
else:
information += "with no distortion correction"
qaImages.setInformation(information)
return qaImages
def qaSupplier(self):
"""Create and supply images for the report generated by qa task
"""
qaImages = Images()
information = "Warning: due to storage restriction, streamlines were " \
"downsampled. Even if there is no difference in structural " \
"connectivity, you should be careful before computing any " \
"metrics along these streamlines.\n To run toad without this " \
"downsampling, please refer to the documentation."
qaImages.setInformation(information)
#get images
norm = self.getRegistrationImage("norm", "resample")
mask253 = self.getMaskingImage('aparc_aseg', ['253', 'mask'])
#images production
if self.__nbDirections <= 45 and not self.get('forceHardi'):
tags = (
(self.__tckDetRoiTrk,
'fiber crossing aparc_aseg area 253 from a deterministic tensor streamlines'
),
(self.__tckProbRoiTrk,
'fiber crossing aparc_aseg area 253 from a probabilistic tensor streamlines'
),
)
else:
tags = (
(self.__tckgenRoiTrk,
'fiber crossing aparc_aseg area 253 from a probabilistic hardi streamlines'
),
(self.__tcksiftRoiTrk,
'fiber crossing aparc_aseg area 253 from a probabilistic hardi streamlines with sift'
),
)
for data, description in tags:
if data is not None:
imageQa = self.plotTrk(data, norm, mask253, None, None, 65,
-70, 2.5, 185)
qaImages.append((imageQa, description))
return qaImages
def qaSupplier(self):
"""Create and supply images for the report generated by qa task
"""
qaImages = Images()
softwareName = 'dipy'
#Set information
information = "Fit method: {}".format(self.get('fitMethod'))
qaImages.setInformation(information)
# mask image
mask = self.getRegistrationImage('mask', 'resample')
# Produce tensor ellipsoids image
dwi = self.getUpsamplingImage('dwi', 'upsample')
cc = self.getMaskingImage('aparc_aseg', ['253', 'mask'])
ellipsoidsQa = self.plotReconstruction(self.__fit, mask, cc, 'tensor',
dwi)
qaImages.append(
(ellipsoidsQa,
'Coronal slice of tensor ellipsoids in the Corpus Callosum'))
# Build qa images
tags = (
#(['tensor', 'rgb'], 'RGB map'),
('fa', 0.7, 'Fractional anisotropy'),
('ad', 0.005, 'Axial Diffusivity'),
('md', 0.005, 'Mean Diffusivity'),
('rd', 0.005, 'Radial Diffusivity'),
)
for postfix, vmax, description in tags:
image = self.getImage('dwi', postfix)
if image:
imageQa = self.plot3dVolume(image,
fov=mask,
vmax=vmax,
colorbar=True,
postfix=softwareName)
qaImages.append((imageQa, description))
return qaImages
def qaSupplier(self):
"""Create and supply images for the report generated by qa task
"""
qaImages = Images()
algorithm = self.get("algorithm")
#Information on denoising algorithm
information = 'Algorithm {} is set'.format(algorithm)
if self.matlabWarning:
information += ' but matlab is not available on this server'
qaImages.setInformation(information)
#Get images
dwi = self.__getDwiImage()
dwiDenoised = self.getImage('dwi', 'denoise')
brainMask = self.getCorrectionImage('mask', 'corrected')
b0 = self.getCorrectionImage('b0', 'corrected')
noiseMask = self.getImage('dwi', 'noise_mask')
#Build qa images
if dwiDenoised:
dwiDenoisedGif = self.buildName(dwiDenoised, None, 'gif')
dwiCompareGif = self.buildName(dwiDenoised, 'compare', 'gif')
self.slicerGif(dwiDenoised, dwiDenoisedGif, boundaries=brainMask)
self.slicerGifCompare(dwi, dwiDenoised, dwiCompareGif, boundaries=brainMask)
qaImages.extend(Images(
(dwiDenoisedGif, 'Denoised diffusion image'),
(dwiCompareGif, 'Before and after denoising'),
))
if algorithm == "nlmeans":
sigmaPng = self.buildName(dwiDenoised, 'sigma', 'png')
noiseMaskPng = self.buildName(noiseMask, None, 'png')
self.plotSigma(self.sigmaVector, sigmaPng)
self.slicerPng(b0, noiseMaskPng, maskOverlay=noiseMask, boundaries=noiseMask)
qaImages.extend(Images(
(sigmaPng, 'Sigmas from nlmeans algorithm'),
(noiseMaskPng, 'Noise mask from nlmeans algorithm'),
))
return qaImages
def qaSupplier(self):
"""Create and supply images for the report generated by qa task
"""
qaImages = Images()
softwareName = 'dipy'
#Set information
information = "Fit method: {}".format(self.get('fitMethod'))
qaImages.setInformation(information)
# mask image
mask = self.getRegistrationImage('mask', 'resample')
# Produce tensor ellipsoids image
dwi = self.getUpsamplingImage('dwi', 'upsample')
cc = self.getMaskingImage('aparc_aseg', ['253','mask'])
ellipsoidsQa = self.plotReconstruction(
self.__fit, mask, cc, 'tensor', dwi)
qaImages.append((
ellipsoidsQa,
'Coronal slice of tensor ellipsoids in the Corpus Callosum'))
# Build qa images
tags = (
#(['tensor', 'rgb'], 'RGB map'),
('fa', 0.7, 'Fractional anisotropy'),
('ad', 0.005, 'Axial Diffusivity'),
('md', 0.005, 'Mean Diffusivity'),
('rd', 0.005, 'Radial Diffusivity'),
)
for postfix, vmax, description in tags:
image = self.getImage('dwi', postfix)
if image:
imageQa = self.plot3dVolume(
image, fov=mask, vmax=vmax,
colorbar=True, postfix=softwareName)
qaImages.append((imageQa, description))
return qaImages
def qaSupplier(self):
"""Create and supply images for the report generated by qa task
"""
qaImages = Images()
softwareName = 'fsl'
#Set information
information = "Fit method: {}".format(self.get('fitMethod'))
qaImages.setInformation(information)
#Get images
mask = self.getRegistrationImage('mask', 'resample')
#Build qa images
tags = (
('fa', 0.7, 'Fractional anisotropy'),
('ad', 0.005, 'Axial Diffusivity'),
('md', 0.005, 'Mean Diffusivity'),
('rd', 0.005, 'Radial Diffusivity'),
)
for postfix, vmax, description in tags:
image = self.getImage('dwi', postfix)
if image:
imageQa = self.plot3dVolume(image,
fov=mask,
vmax=vmax,
colorbar=True,
postfix=softwareName)
qaImages.append((imageQa, description))
#Build SSE image
sse = self.getImage('dwi', 'sse')
sseQa = self.plot3dVolume(sse,
fov=mask,
postfix=softwareName,
colorbar=True)
qaImages.append((sseQa, 'Sum of squared errors'))
return qaImages
def qaSupplier(self):
"""Create and supply images for the report generated by qa task
"""
qaImages = Images()
information = "Warning: due to storage restriction, streamlines were " \
"downsampled. Even if there is no difference in structural " \
"connectivity, you should be careful before computing any " \
"metrics along these streamlines.\n To run toad without this " \
"downsampling, please refer to the documentation."
qaImages.setInformation(information)
#get images
norm = self.getRegistrationImage("norm", "resample")
mask253 = self.getMaskingImage('aparc_aseg',['253','mask'])
#images production
if self.__nbDirections <= 45 and not self.get('forceHardi'):
tags = (
(self.__tckDetRoiTrk,
'fiber crossing aparc_aseg area 253 from a deterministic tensor streamlines'),
(self.__tckProbRoiTrk,
'fiber crossing aparc_aseg area 253 from a probabilistic tensor streamlines'),
)
else:
tags = (
(self.__tckgenRoiTrk,
'fiber crossing aparc_aseg area 253 from a probabilistic hardi streamlines'),
(self.__tcksiftRoiTrk,
'fiber crossing aparc_aseg area 253 from a probabilistic hardi streamlines with sift'),
)
for data, description in tags:
if data is not None:
imageQa = self.plotTrk(data, norm, mask253, None, None, 65, -70, 2.5, 185)
qaImages.append((imageQa, description))
return qaImages
def qaSupplier(self):
"""Create and supply images for the report generated by qa task
"""
qaImages = Images()
softwareName = 'fsl'
#Set information
information = "Fit method: {}".format(self.get('fitMethod'))
qaImages.setInformation(information)
#Get images
mask = self.getRegistrationImage('mask', 'resample')
#Build qa images
tags = (
('fa', 0.7, 'Fractional anisotropy'),
('ad', 0.005, 'Axial Diffusivity'),
('md', 0.005, 'Mean Diffusivity'),
('rd', 0.005, 'Radial Diffusivity'),
)
for postfix, vmax, description in tags:
image = self.getImage('dwi', postfix)
if image:
imageQa = self.plot3dVolume(
image, fov=mask, vmax=vmax,
colorbar=True, postfix=softwareName)
qaImages.append((imageQa, description))
#Build SSE image
sse = self.getImage('dwi', 'sse')
sseQa = self.plot3dVolume(
sse, fov=mask, postfix=softwareName, colorbar=True)
qaImages.append((sseQa, 'Sum of squared errors'))
return qaImages
def qaSupplier(self):
"""Create and supply images for the report generated by qa task
"""
qaImages = Images()
#Information on denoising algorithm
information = 'Denoising was done using the {} algorithm'.format(self.algorithm)
if self.matlabWarning:
information = "Algorithm `aonlm` or `lpca` was set for the " \
"denoising, but Matlab is not available for this server. "\
"Please install and configure Matlab or set `ignore: True`"\
" into the [denoising] section of your config.cfg file."
qaImages.extend(Images((False, 'Denoised diffusion image')))
qaImages.setInformation(information)
#Get images
dwi = self.getPreparationImage("dwi")
dwiDenoised = self.getImage('dwi', 'denoise')
brainMask = self.getImage('mask', 'resample')
b0 = self.getImage('b0')
noiseMask = self.getImage('dwi', 'noise_mask')
noise = self.getImage('dwi','noise')
residuals = self.getImage('dwi', 'residuals')
#Build qa images
if dwiDenoised:
dwiDenoisedQa = self.plot4dVolume(dwiDenoised, fov=brainMask)
qaImages.append((dwiDenoisedQa, 'Denoised diffusion image'))
dwiCompareQa = self.compare4dVolumes(
dwi, dwiDenoised, fov=brainMask)
qaImages.append((dwiCompareQa, 'Before and after denoising'))
if self.algorithm == "nlmeans":
if self.sigmaVector != None:
sigmaQa = self.plotSigma(self.sigmaVector, dwiDenoised)
qaImages.append(
(sigmaQa, 'Sigmas from the nlmeans algorithm'))
if noiseMask:
noiseMaskQa = self.plot3dVolume(
b0, edges=noiseMask, fov=noiseMask)
qaImages.append(
(noiseMaskQa, 'Noise mask from the mp-pca algorithm'))
if self.algorithm == "mp-pca":
if noise:
noiseQa = self.plot3dVolume(
noise, fov=noise)
qaImages.append(
(noiseQa, 'Noise from the mp-pca algorithm'))
if residuals:
resQa = self.plot4dVolume(
residuals, fov=residuals)
qaImages.append(
(resQa, 'Residuals from the mp-pca algorithm'))
return qaImages
