def tensor_pipeline(self, **name_maps): # @UnusedVariable
"""
Fits the apparrent diffusion tensor (DT) to each voxel of the image
"""
# inputs=[FilesetSpec('bias_correct', nifti_gz_format),
# FilesetSpec('grad_dirs', fsl_bvecs_format),
# FilesetSpec('bvalues', fsl_bvals_format),
# FilesetSpec('brain_mask', nifti_gz_format)],
# outputs=[FilesetSpec('tensor', nifti_gz_format)],
pipeline = self.new_pipeline(
name='tensor',
desc=("Estimates the apparent diffusion tensor in each "
"voxel"),
references=[],
name_maps=name_maps)
# Create tensor fit node
dwi2tensor = pipeline.add(
'dwi2tensor',
FitTensor())
dwi2tensor.inputs.out_file = 'dti.nii.gz'
# Gradient merge node
fsl_grads = pipeline.add("fsl_grads", MergeTuple(2))
# Connect nodes
pipeline.connect(fsl_grads, 'out', dwi2tensor, 'grad_fsl')
# Connect to inputs
pipeline.connect_input('grad_dirs', fsl_grads, 'in1')
pipeline.connect_input('bvalues', fsl_grads, 'in2')
pipeline.connect_input('bias_correct', dwi2tensor, 'in_file')
pipeline.connect_input('brain_mask', dwi2tensor, 'in_mask')
# Connect to outputs
pipeline.connect_output('tensor', dwi2tensor, 'out_file')
# Check inputs/output are connected
return pipeline
def fod_pipeline(self, **name_maps): # @UnusedVariable
"""
Estimates the fibre orientation distribution (FOD) using constrained
spherical deconvolution
Parameters
----------
"""
# inputs=[FilesetSpec('bias_correct', nifti_gz_format),
# FilesetSpec('grad_dirs', fsl_bvecs_format),
# FilesetSpec('bvalues', fsl_bvals_format),
# FilesetSpec('wm_response', text_format),
# FilesetSpec('brain_mask', nifti_gz_format)],
# outputs=[FilesetSpec('fod', nifti_gz_format)],
pipeline = self.new_pipeline(
name='fod',
desc=("Estimates the fibre orientation distribution in each"
" voxel"),
references=[mrtrix_cite],
name_maps=name_maps)
if self.branch('fod_algorithm', 'msmt_csd'):
pipeline.add_input(FilesetSpec('gm_response', text_format))
pipeline.add_input(FilesetSpec('csf_response', text_format))
# Create fod fit node
dwi2fod = pipeline.add(
'dwi2fod',
EstimateFOD(),
requirements=[mrtrix_req.v('3.0rc3')])
dwi2fod.inputs.algorithm = self.parameter('fod_algorithm')
# Gradient merge node
fsl_grads = pipeline.add("fsl_grads", MergeTuple(2))
# Connect nodes
pipeline.connect(fsl_grads, 'out', dwi2fod, 'grad_fsl')
# Connect to inputs
pipeline.connect_input('grad_dirs', fsl_grads, 'in1')
pipeline.connect_input('bvalues', fsl_grads, 'in2')
pipeline.connect_input('bias_correct', dwi2fod, 'in_file')
pipeline.connect_input('wm_response', dwi2fod, 'wm_txt')
pipeline.connect_input('brain_mask', dwi2fod, 'mask_file')
# Connect to outputs
pipeline.connect_output('wm_odf', dwi2fod, 'wm_odf')
# If multi-tissue
if self.multi_tissue:
pipeline.connect_input('gm_response', dwi2fod, 'gm_txt')
pipeline.connect_input('csf_response', dwi2fod, 'csf_txt')
dwi2fod.inputs.gm_odf = 'gm.mif'
dwi2fod.inputs.csf_odf = 'csf.mif'
pipeline.connect_output('gm_odf', dwi2fod, 'gm_odf')
pipeline.connect_output('csf_odf', dwi2fod, 'csf_odf')
# Check inputs/output are connected
return pipeline
def response_pipeline(self, **name_maps): # @UnusedVariable
"""
Estimates the fibre orientation distribution (FOD) using constrained
spherical deconvolution
Parameters
----------
response_algorithm : str
Algorithm used to estimate the response
"""
# outputs = [FilesetSpec('wm_response', text_format)]
# if self.branch('response_algorithm', ('dhollander', 'msmt_5tt')):
# outputs.append(FilesetSpec('gm_response', text_format))
# outputs.append(FilesetSpec('csf_response', text_format))
# inputs=[FilesetSpec('bias_correct', nifti_gz_format),
# FilesetSpec('grad_dirs', fsl_bvecs_format),
# FilesetSpec('bvalues', fsl_bvals_format),
# FilesetSpec('brain_mask', nifti_gz_format)],
# outputs=outputs,
pipeline = self.new_pipeline(
name='response',
desc=("Estimates the fibre response function"),
references=[mrtrix_cite],
name_maps=name_maps)
# Create fod fit node
response = pipeline.add(
'response',
ResponseSD(),
requirements=[mrtrix_req.v('3.0rc3')])
response.inputs.algorithm = self.parameter('response_algorithm')
# Gradient merge node
fsl_grads = pipeline.add(
"fsl_grads",
MergeTuple(2))
# Connect nodes
pipeline.connect(fsl_grads, 'out', response, 'grad_fsl')
# Connect to inputs
pipeline.connect_input('grad_dirs', fsl_grads, 'in1')
pipeline.connect_input('bvalues', fsl_grads, 'in2')
pipeline.connect_input('bias_correct', response, 'in_file')
pipeline.connect_input('brain_mask', response, 'in_mask')
# Connect to outputs
pipeline.connect_output('wm_response', response, 'wm_file')
if self.multi_tissue:
response.inputs.gm_file = 'gm.txt'
response.inputs.csf_file = 'csf.txt'
pipeline.connect_output('gm_response', response, 'gm_file')
pipeline.connect_output('csf_response', response, 'csf_file')
# Check inputs/output are connected
return pipeline
def extract_b0_pipeline(self, **name_maps): # @UnusedVariable
"""
Extracts the b0 images from a DWI study and takes their mean
"""
# inputs=[FilesetSpec('bias_correct', nifti_gz_format),
# FilesetSpec('grad_dirs', fsl_bvecs_format),
# FilesetSpec('bvalues', fsl_bvals_format)],
# outputs=[FilesetSpec('b0', nifti_gz_format)],
pipeline = self.new_pipeline(
name='extract_b0',
desc="Extract b0 image from a DWI study",
references=[mrtrix_cite],
name_maps=name_maps)
# Gradient merge node
fsl_grads = pipeline.add("fsl_grads", MergeTuple(2))
# Extraction node
extract_b0s = pipeline.add(
'extract_b0s', ExtractDWIorB0(),
requirements=[mrtrix_req.v('3.0rc3')])
extract_b0s.inputs.bzero = True
extract_b0s.inputs.quiet = True
# FIXME: Need a registration step before the mean
# Mean calculation node
mean = pipeline.add(
"mean",
MRMath(),
requirements=[mrtrix_req.v('3.0rc3')])
mean.inputs.axis = 3
mean.inputs.operation = 'mean'
mean.inputs.quiet = True
# Convert to Nifti
mrconvert = pipeline.add("output_conversion", MRConvert(),
requirements=[mrtrix_req.v('3.0rc3')])
mrconvert.inputs.out_ext = '.nii.gz'
mrconvert.inputs.quiet = True
# Connect inputs
pipeline.connect_input('bias_correct', extract_b0s, 'in_file')
pipeline.connect_input('grad_dirs', fsl_grads, 'in1')
pipeline.connect_input('bvalues', fsl_grads, 'in2')
# Connect between nodes
pipeline.connect(extract_b0s, 'out_file', mean, 'in_files')
pipeline.connect(fsl_grads, 'out', extract_b0s, 'grad_fsl')
pipeline.connect(mean, 'out_file', mrconvert, 'in_file')
# Connect outputs
pipeline.connect_output('b0', mrconvert, 'out_file')
# Check inputs/outputs are connected
return pipeline
def fsl_grads(self, pipeline, coregistered=True):
"Adds and returns a node to the pipeline to merge the FSL grads and "
"bvecs"
try:
fslgrad = pipeline.node('fslgrad')
except ArcanaNameError:
if self.is_coregistered and coregistered:
grad_dirs = 'grad_dirs_coreg'
else:
grad_dirs = 'grad_dirs'
# Gradient merge node
fslgrad = pipeline.add(
"fslgrad",
MergeTuple(2),
inputs={
'in1': (grad_dirs, fsl_bvecs_format),
'in2': ('bvalues', fsl_bvals_format)})
return (fslgrad, 'out')
def brain_extraction_pipeline(self, **name_maps): # @UnusedVariable @IgnorePep8
"""
Generates a whole brain mask using MRtrix's 'dwi2mask' command
Parameters
----------
mask_tool: Str
Can be either 'bet' or 'dwi2mask' depending on which mask tool you
want to use
"""
if self.branch('brain_extract_method', 'mrtrix'):
pipeline = self.new_pipeline(
'brain_extraction',
desc="Generate brain mask from b0 images",
references=[mrtrix_cite],
name_maps=name_maps)
# Gradient merge node
grad_fsl = pipeline.add(
"grad_fsl",
MergeTuple(2),
inputs={
'in1': ('grad_dirs', fsl_bvecs_format),
'in2': ('bvalues', fsl_bvals_format)})
# Create mask node
pipeline.add(
'dwi2mask',
BrainMask(
out_file='brain_mask.nii.gz'),
inputs={
'in_file': ('preproc', nifti_gz_format)},
connect={
'grad_fsl': (grad_fsl, 'out')},
outputs={
'out_file': ('brain_mask', nifti_gz_format)},
requirements=[mrtrix_req.v('3.0rc3')])
else:
pipeline = super(DmriStudy, self).brain_extraction_pipeline(
**name_maps)
return pipeline
def bias_correct_pipeline(self, **name_maps): # @UnusedVariable @IgnorePep8
"""
Corrects B1 field inhomogeneities
"""
# inputs=[FilesetSpec('preproc', nifti_gz_format),
# FilesetSpec('brain_mask', nifti_gz_format),
# FilesetSpec('grad_dirs', fsl_bvecs_format),
# FilesetSpec('bvalues', fsl_bvals_format)],
# outputs=[FilesetSpec('bias_correct', nifti_gz_format)],
bias_method = self.parameter('bias_correct_method')
pipeline = self.new_pipeline(
name='bias_correct',
desc="Corrects for B1 field inhomogeneity",
references=[fast_cite,
(n4_cite if bias_method == 'ants' else fsl_cite)],
name_maps=name_maps)
# Create bias correct node
bias_correct = pipeline.add(
"bias_correct", DWIBiasCorrect(),
requirements=(
[mrtrix_req.v('3.0rc3')] +
[ants_req.v('2.0')
if bias_method == 'ants' else fsl_req.v('5.0.9')]))
bias_correct.inputs.method = bias_method
# Gradient merge node
fsl_grads = pipeline.add(
"fsl_grads",
MergeTuple(2))
# Connect nodes
pipeline.connect(fsl_grads, 'out', bias_correct, 'grad_fsl')
# Connect to inputs
pipeline.connect_input('grad_dirs', fsl_grads, 'in1')
pipeline.connect_input('bvalues', fsl_grads, 'in2')
pipeline.connect_input('preproc', bias_correct, 'in_file')
pipeline.connect_input('brain_mask', bias_correct, 'mask')
# Connect to outputs
pipeline.connect_output('bias_correct', bias_correct, 'out_file')
# Check inputs/output are connected
return pipeline
def global_tracking_pipeline(self, **name_maps):
# inputs=[FilesetSpec('fod', mrtrix_format),
# FilesetSpec('bias_correct', nifti_gz_format),
# FilesetSpec('brain_mask', nifti_gz_format),
# FilesetSpec('wm_response', text_format),
# FilesetSpec('grad_dirs', fsl_bvecs_format),
# FilesetSpec('bvalues', fsl_bvals_format)],
# outputs=[FilesetSpec('global_tracks', mrtrix_track_format)],
pipeline = self.new_pipeline(
name='global_tracking',
desc="Extract b0 image from a DWI study",
references=[mrtrix_cite],
name_maps=name_maps)
tck = pipeline.add(
'tracking',
Tractography())
tck.inputs.n_tracks = self.parameter('num_global_tracks')
tck.inputs.cutoff = self.parameter(
'global_tracks_cutoff')
mask = pipeline.add(
'mask',
DWI2Mask())
# Add gradients to input image
fsl_grads = pipeline.add(
"fsl_grads",
MergeTuple(2))
pipeline.connect(fsl_grads, 'out', mask, 'grad_fsl')
pipeline.connect(mask, 'out_file', tck, 'seed_image')
pipeline.connect_input('wm_odf', tck, 'in_file')
pipeline.connect_input('bias_correct', mask, 'in_file')
pipeline.connect_input('grad_dirs', fsl_grads, 'in1')
pipeline.connect_input('bvalues', fsl_grads, 'in2')
pipeline.connect_output('global_tracks', tck, 'out_file')
return pipeline
def intensity_normalisation_pipeline(self, **name_maps):
# inputs=[FilesetSpec('bias_correct', nifti_gz_format),
# FilesetSpec('brain_mask', nifti_gz_format),
# FilesetSpec('grad_dirs', fsl_bvecs_format),
# FilesetSpec('bvalues', fsl_bvals_format)],
# outputs=[FilesetSpec('norm_intensity', mrtrix_format),
# FilesetSpec('norm_intens_fa_template', mrtrix_format,
# frequency='per_study'),
# FilesetSpec('norm_intens_wm_mask', mrtrix_format,
# frequency='per_study')],
pipeline = self.new_pipeline(
name='intensity_normalization',
desc="Corrects for B1 field inhomogeneity",
references=[mrtrix_req.v('3.0rc3')],
name_maps=name_maps)
# Convert from nifti to mrtrix format
grad_merge = pipeline.add("grad_merge", MergeTuple(2))
mrconvert = pipeline.add('mrconvert', MRConvert())
mrconvert.inputs.out_ext = '.mif'
# Set up join nodes
fields = ['dwis', 'masks', 'subject_ids', 'visit_ids']
join_subjects = pipeline.add(
'join_subjects',
IdentityInterface(fields),
joinsource=self.SUBJECT_ID,
joinfield=fields)
join_visits = pipeline.add(
'join_visits',
Chain(fields),
joinsource=self.VISIT_ID,
joinfield=fields)
# Set up expand nodes
select = pipeline.add(
'expand', SelectSession())
# Intensity normalization
intensity_norm = pipeline.add(
'dwiintensitynorm', DWIIntensityNorm())
# Connect inputs
pipeline.connect_input('bias_correct', mrconvert, 'in_file')
pipeline.connect_input('grad_dirs', grad_merge, 'in1')
pipeline.connect_input('bvalues', grad_merge, 'in2')
pipeline.connect_subject_id(join_subjects, 'subject_ids')
pipeline.connect_visit_id(join_subjects, 'visit_ids')
pipeline.connect_subject_id(select, 'subject_id')
pipeline.connect_visit_id(select, 'visit_id')
pipeline.connect_input('brain_mask', join_subjects, 'masks')
# Internal connections
pipeline.connect(grad_merge, 'out', mrconvert, 'grad_fsl')
pipeline.connect(mrconvert, 'out_file', join_subjects, 'dwis')
pipeline.connect(join_subjects, 'dwis', join_visits, 'dwis')
pipeline.connect(join_subjects, 'masks', join_visits, 'masks')
pipeline.connect(join_subjects, 'subject_ids', join_visits,
'subject_ids')
pipeline.connect(join_subjects, 'visit_ids', join_visits,
'visit_ids')
pipeline.connect(join_visits, 'dwis', intensity_norm, 'in_files')
pipeline.connect(join_visits, 'masks', intensity_norm, 'masks')
pipeline.connect(join_visits, 'subject_ids', select, 'subject_ids')
pipeline.connect(join_visits, 'visit_ids', select, 'visit_ids')
pipeline.connect(intensity_norm, 'out_files', select, 'items')
# Connect outputs
pipeline.connect_output('norm_intensity', select, 'item')
pipeline.connect_output('norm_intens_fa_template', intensity_norm,
'fa_template')
pipeline.connect_output('norm_intens_wm_mask', intensity_norm,
'wm_mask')
return pipeline
def preprocess_pipeline(self, **name_maps): # @UnusedVariable @IgnorePep8
"""
Performs a series of FSL preprocessing steps, including Eddy and Topup
Parameters
----------
phase_dir : str{AP|LR|IS}
The phase encode direction
"""
# Determine whether we can correct for distortion, i.e. if reference
# scans are provided
# Include all references
references = [fsl_cite, eddy_cite, topup_cite,
distort_correct_cite]
if self.branch('preproc_denoise'):
references.extend(dwidenoise_cites)
pipeline = self.new_pipeline(
name='preprocess',
name_maps=name_maps,
desc=(
"Preprocess dMRI studies using distortion correction"),
references=references)
# Create nodes to gradients to FSL format
if self.input('magnitude').format == dicom_format:
extract_grad = pipeline.add(
"extract_grad",
ExtractFSLGradients(),
inputs={
'in_file': ('magnitude', dicom_format)},
outputs={
'bvecs_file': ('grad_dirs', fsl_bvecs_format),
'bvals_file': ('bvalues', fsl_bvals_format)},
requirements=[mrtrix_req.v('3.0rc3')])
grad_fsl_kwargs = {
'connect': {'in1': (extract_grad, 'bvecs_file'),
'in2': (extract_grad, 'bvals_file')}}
elif self.provided('grad_dirs') and self.provided('bvalues'):
grad_fsl_kwargs = {
'inputs': {'in1': ('grad_dirs', fsl_bvecs_format),
'in2': ('bvalues', fsl_bvals_format)}}
else:
raise ArcanaDesignError(
"Either input 'magnitude' image needs to be in DICOM format "
"or gradient directions and b-values need to be explicitly "
"provided to {}".format(self))
# Gradient merge node
grad_fsl = pipeline.add(
"grad_fsl",
MergeTuple(2),
**grad_fsl_kwargs)
# Denoise the dwi-scan
if self.branch('preproc_denoise'):
# Run denoising
denoise = pipeline.add(
'denoise',
DWIDenoise(),
inputs={
'in_file': ('magnitude', nifti_gz_format)},
requirements=[mrtrix_req.v('3.0rc3')])
# Calculate residual noise
subtract_operands = pipeline.add(
'subtract_operands',
Merge(2),
inputs={
'in1': ('magnitude', nifti_gz_format)},
connect={
'in2': (denoise, 'noise')})
pipeline.add(
'subtract',
MRCalc(
operation='subtract'),
connect={
'operands': (subtract_operands, 'out')},
outputs={
'out_file': ('noise_residual', mrtrix_format)},
requirements=[mrtrix_req.v('3.0rc3')])
# Preproc kwargs
preproc_kwargs = {}
if (self.provided('dwi_reference') or
self.provided('reverse_phase')):
# Extract b=0 volumes
dwiextract = pipeline.add(
'dwiextract',
ExtractDWIorB0(
bzero=True,
out_ext='.nii.gz'),
inputs={
'in_file': ('magnitude', dicom_format)},
requirements=[mrtrix_req.v('3.0rc3')])
# Get first b=0 from dwi b=0 volumes
mrconvert = pipeline.add(
"mrconvert",
MRConvert(
coord=(3, 0)),
connect={
'in_file': (dwiextract, 'out_file')},
requirements=[mrtrix_req.v('3.0rc3')])
# Concatenate extracted forward rpe with reverse rpe
mrcat = pipeline.add(
'mrcat',
MRCat(),
inputs={
'second_scan': ((
'dwi_reference' if self.provided('dwi_reference')
else 'reverse_phase'), mrtrix_format)},
connect={
'first_scan': (mrconvert, 'out_file')},
requirements=[mrtrix_req.v('3.0rc3')])
# Create node to assign the right PED to the diffusion
prep_dwi = pipeline.add(
'prepare_dwi',
PrepareDWI(),
inputs={
'pe_dir': ('ped', float),
'ped_polarity': ('pe_angle', float)})
preproc_kwargs['rpe_pair'] = True
distortion_correction = True
preproc_conns = {'connect': {'se_epi': (mrcat, 'out_file')}}
else:
distortion_correction = False
preproc_kwargs['rpe_none'] = True
preproc_conns = {}
if self.parameter('preproc_pe_dir') is not None:
preproc_kwargs['pe_dir'] = self.parameter('preproc_pe_dir')
preproc = pipeline.add(
'dwipreproc',
DWIPreproc(
no_clean_up=True,
out_file_ext='.nii.gz',
# FIXME: Need to determine this programmatically
# eddy_parameters = '--data_is_shelled '
temp_dir='dwipreproc_tempdir',
**preproc_kwargs),
connect={
'grad_fsl': (grad_fsl, 'out')},
outputs={
'eddy_parameters': ('eddy_par', eddy_par_format)},
requirements=[mrtrix_req.v('3.0rc3'), fsl_req.v('5.0.10')],
wall_time=60,
**preproc_conns)
if self.branch('preproc_denoise'):
pipeline.connect(denoise, 'out_file', preproc, 'in_file')
else:
pipeline.connect_input('magnitude', preproc, 'in_file',
nifti_gz_format)
if distortion_correction:
pipeline.connect(prep_dwi, 'pe', preproc, 'pe_dir')
# Create node to reorient preproc out_file
pipeline.add(
'fslreorient2std',
fsl.utils.Reorient2Std(),
connect={
'in_file': (preproc, 'out_file')},
outputs={
'out_file': ('preproc', nifti_gz_format)},
requirements=[fsl_req.v('5.0.9')])
return pipeline
def preprocess_pipeline(self, **name_maps):
"""
Performs a series of FSL preprocessing steps, including Eddy and Topup
Parameters
----------
phase_dir : str{AP|LR|IS}
The phase encode direction
"""
# Determine whether we can correct for distortion, i.e. if reference
# scans are provided
# Include all references
references = [fsl_cite, eddy_cite, topup_cite,
distort_correct_cite, n4_cite]
if self.branch('preproc_denoise'):
references.extend(dwidenoise_cites)
pipeline = self.new_pipeline(
name='preprocess',
name_maps=name_maps,
desc=(
"Preprocess dMRI studies using distortion correction"),
citations=references)
# Create nodes to gradients to FSL format
if self.input('series').format == dicom_format:
extract_grad = pipeline.add(
"extract_grad",
ExtractFSLGradients(),
inputs={
'in_file': ('series', dicom_format)},
outputs={
'grad_dirs': ('bvecs_file', fsl_bvecs_format),
'bvalues': ('bvals_file', fsl_bvals_format)},
requirements=[mrtrix_req.v('3.0rc3')])
grad_fsl_inputs = {'in1': (extract_grad, 'bvecs_file'),
'in2': (extract_grad, 'bvals_file')}
elif self.provided('grad_dirs') and self.provided('bvalues'):
grad_fsl_inputs = {'in1': ('grad_dirs', fsl_bvecs_format),
'in2': ('bvalues', fsl_bvals_format)}
else:
raise BananaUsageError(
"Either input 'magnitude' image needs to be in DICOM format "
"or gradient directions and b-values need to be explicitly "
"provided to {}".format(self))
# Gradient merge node
grad_fsl = pipeline.add(
"grad_fsl",
MergeTuple(2),
inputs=grad_fsl_inputs)
gradients = (grad_fsl, 'out')
# Create node to reorient preproc out_file
if self.branch('reorient2std'):
reorient = pipeline.add(
'fslreorient2std',
fsl.utils.Reorient2Std(
output_type='NIFTI_GZ'),
inputs={
'in_file': ('series', nifti_gz_format)},
requirements=[fsl_req.v('5.0.9')])
reoriented = (reorient, 'out_file')
else:
reoriented = ('series', nifti_gz_format)
# Denoise the dwi-scan
if self.branch('preproc_denoise'):
# Run denoising
denoise = pipeline.add(
'denoise',
DWIDenoise(),
inputs={
'in_file': reoriented},
requirements=[mrtrix_req.v('3.0rc3')])
# Calculate residual noise
subtract_operands = pipeline.add(
'subtract_operands',
Merge(2),
inputs={
'in1': reoriented,
'in2': (denoise, 'noise')})
pipeline.add(
'subtract',
MRCalc(
operation='subtract'),
inputs={
'operands': (subtract_operands, 'out')},
outputs={
'noise_residual': ('out_file', mrtrix_image_format)},
requirements=[mrtrix_req.v('3.0rc3')])
denoised = (denoise, 'out_file')
else:
denoised = reoriented
# Preproc kwargs
preproc_kwargs = {}
preproc_inputs = {'in_file': denoised,
'grad_fsl': gradients}
if self.provided('reverse_phase'):
if self.provided('magnitude', default_okay=False):
dwi_reference = ('magnitude', mrtrix_image_format)
else:
# Extract b=0 volumes
dwiextract = pipeline.add(
'dwiextract',
ExtractDWIorB0(
bzero=True,
out_ext='.nii.gz'),
inputs={
'in_file': denoised,
'fslgrad': gradients},
requirements=[mrtrix_req.v('3.0rc3')])
# Get first b=0 from dwi b=0 volumes
extract_first_b0 = pipeline.add(
"extract_first_vol",
MRConvert(
coord=(3, 0)),
inputs={
'in_file': (dwiextract, 'out_file')},
requirements=[mrtrix_req.v('3.0rc3')])
dwi_reference = (extract_first_b0, 'out_file')
# Concatenate extracted forward rpe with reverse rpe
combined_images = pipeline.add(
'combined_images',
MRCat(),
inputs={
'first_scan': dwi_reference,
'second_scan': ('reverse_phase', mrtrix_image_format)},
requirements=[mrtrix_req.v('3.0rc3')])
# Create node to assign the right PED to the diffusion
prep_dwi = pipeline.add(
'prepare_dwi',
PrepareDWI(),
inputs={
'pe_dir': ('ped', float),
'ped_polarity': ('pe_angle', float)})
preproc_kwargs['rpe_pair'] = True
distortion_correction = True
preproc_inputs['se_epi'] = (combined_images, 'out_file')
else:
distortion_correction = False
preproc_kwargs['rpe_none'] = True
if self.parameter('preproc_pe_dir') is not None:
preproc_kwargs['pe_dir'] = self.parameter('preproc_pe_dir')
preproc = pipeline.add(
'dwipreproc',
DWIPreproc(
no_clean_up=True,
out_file_ext='.nii.gz',
# FIXME: Need to determine this programmatically
# eddy_parameters = '--data_is_shelled '
temp_dir='dwipreproc_tempdir',
**preproc_kwargs),
inputs=preproc_inputs,
outputs={
'eddy_par': ('eddy_parameters', eddy_par_format)},
requirements=[mrtrix_req.v('3.0rc3'), fsl_req.v('5.0.10')],
wall_time=60)
if distortion_correction:
pipeline.connect(prep_dwi, 'pe', preproc, 'pe_dir')
mask = pipeline.add(
'dwi2mask',
BrainMask(
out_file='brainmask.nii.gz'),
inputs={
'in_file': (preproc, 'out_file'),
'grad_fsl': gradients},
requirements=[mrtrix_req.v('3.0rc3')])
# Create bias correct node
pipeline.add(
"bias_correct",
DWIBiasCorrect(
method='ants'),
inputs={
'grad_fsl': gradients, # internal
'in_file': (preproc, 'out_file'),
'mask': (mask, 'out_file')},
outputs={
'series_preproc': ('out_file', nifti_gz_format)},
requirements=[mrtrix_req.v('3.0rc3'), ants_req.v('2.0')])
return pipeline