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 response_pipeline(self, **kwargs): # @UnusedVariable
"""
Estimates the fibre orientation distribution (FOD) using constrained
spherical deconvolution
Parameters
----------
response_algorithm : str
Algorithm used to estimate the response
"""
outputs = [DatasetSpec('wm_response', text_format)]
if self.multi_tissue:
outputs.append(DatasetSpec('gm_response', text_format))
outputs.append(DatasetSpec('csf_response', text_format))
pipeline = self.create_pipeline(
name='response',
inputs=[
DatasetSpec('bias_correct', nifti_gz_format),
DatasetSpec('grad_dirs', fsl_bvecs_format),
DatasetSpec('bvalues', fsl_bvals_format),
DatasetSpec('brain_mask', nifti_gz_format)
],
outputs=outputs,
desc=("Estimates the fibre response function"),
version=1,
citations=[mrtrix_cite],
**kwargs)
# Create fod fit node
response = pipeline.create_node(ResponseSD(),
name='response',
requirements=[mrtrix3_req])
response.inputs.algorithm = self.switch('response_algorithm')
# Gradient merge node
fsl_grads = pipeline.create_node(MergeTuple(2), name="fsl_grads")
# 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 response_pipeline(self, **name_maps):
"""
Estimates the fibre orientation distribution (FOD) using constrained
spherical deconvolution
Parameters
----------
response_algorithm : str
Algorithm used to estimate the response
"""
pipeline = self.new_pipeline(
name='response',
desc=("Estimates the fibre response function"),
citations=[mrtrix_cite],
name_maps=name_maps)
# Create fod fit node
response = pipeline.add(
'response',
ResponseSD(
algorithm=self.parameter('response_algorithm')),
inputs={
'grad_fsl': self.fsl_grads(pipeline),
'in_file': (self.series_preproc_spec_name, nifti_gz_format),
'in_mask': (self.brain_mask_spec_name, nifti_gz_format)},
outputs={
'wm_response': ('wm_file', text_format)},
requirements=[mrtrix_req.v('3.0rc3')])
# Connect to outputs
if self.multi_tissue:
response.inputs.gm_file = 'gm.txt',
response.inputs.csf_file = 'csf.txt',
pipeline.connect_output('gm_response', response, 'gm_file',
text_format)
pipeline.connect_output('csf_response', response, 'csf_file',
text_format)
return pipeline
"FSL Anatomical Processing Script": {FslAnat.__version__: FslAnat},
"mean_image": {MeanImage().version: MeanImage},
"robustfov": {RobustFOV().version: RobustFOV},
"ReconAll": {ReconAll().version: ReconAll},
"SUSAN": {SUSAN().version: SUSAN},
"topup": {TopupWrapper.version: TopupWrapper},
"eddy": {Eddy().version: Eddy},
"denoise": {DWIDenoise().version: DWIDenoise},
"degibbs": {MRDeGibbs().version: MRDeGibbs},
"bias_correct": {DWIBiasCorrect().version: DWIBiasCorrect},
"dwifslpreproc": {DwiFslPreproc.__version__: DwiFslPreproc},
"mrconvert": {MRConvert.__version__: MRConvert},
"dwi2fod": {
ConstrainedSphericalDeconvolution().version: ConstrainedSphericalDeconvolution # noqa: E501
},
"dwi2response": {ResponseSD().version: ResponseSD},
"5ttgen": {Generate5tt().version: Generate5tt},
"dwi2tensor": {Dwi2Tensor.__version__: Dwi2Tensor},
"tensor2metric": {Tensor2metric.__version__: Tensor2metric},
"mrcat": {MRCat.__version__: MRCat},
"dwigradcheck": {DwiGradCheck.__version__: DwiGradCheck},
"Mutual Information Score": {"1.0": MutualInformationScore},
"fMRIPrep": {
FmriPrep2021.__version__: FmriPrep2021,
FmriPrep2022.__version__: FmriPrep2022,
FmriPrep2023.__version__: FmriPrep2023,
FmriPrep2024.__version__: FmriPrep2024,
FmriPrep2025.__version__: FmriPrep2025,
},
}
"versions": [{
"title": ConstrainedSphericalDeconvolution().version or "1.0",
"description":
f"Default ConstrainedSphericalDeconvolution version for nipype {_NIPYPE_VERSION}.", # noqa: E501
"input": DWI2FOD_INPUT_SPECIFICATION,
"output": DWI2FOD_OUTPUT_SPECIFICATION,
"nested_results_attribute": "outputs.get_traitsfree",
}],
},
{
"title":
"dwi2response",
"description":
"Estimate response function(s) for spherical deconvolution using the specified algorithm.", # noqa: E501
"versions": [{
"title": ResponseSD().version or "1.0",
"description":
f"Default ResponseSD version for nipype {_NIPYPE_VERSION}.", # noqa: E501
"input": DWI2RESPONSE_INPUT_SPECIFICATION,
"output": DWI2RESPONSE_OUTPUT_SPECIFICATION,
"nested_results_attribute": "outputs.get_traitsfree",
}],
},
{
"title":
"5ttgen",
"description":
"Generate a 5TT image suitable for ACT using the selected algorithm.", # noqa: E501
"versions": [{
"title": Generate5tt().version or "1.0",
"description":