def test_EstimateFOD_inputs():
input_map = dict(args=dict(argstr='%s',
),
bval_scale=dict(argstr='-bvalue_scaling %s',
),
environ=dict(nohash=True,
usedefault=True,
),
grad_file=dict(argstr='-grad %s',
),
grad_fsl=dict(argstr='-fslgrad %s %s',
),
ignore_exception=dict(nohash=True,
usedefault=True,
),
in_bval=dict(),
in_bvec=dict(argstr='-fslgrad %s %s',
),
in_dirs=dict(argstr='-directions %s',
),
in_file=dict(argstr='%s',
mandatory=True,
position=-3,
),
in_mask=dict(argstr='-mask %s',
),
max_sh=dict(argstr='-lmax %d',
),
n_iter=dict(argstr='-niter %d',
),
neg_lambda=dict(argstr='-neg_lambda %f',
),
nthreads=dict(argstr='-nthreads %d',
nohash=True,
),
out_file=dict(argstr='%s',
mandatory=True,
position=-1,
usedefault=True,
),
response=dict(argstr='%s',
mandatory=True,
position=-2,
),
sh_filter=dict(argstr='-filter %s',
),
shell=dict(argstr='-shell %s',
sep=',',
),
terminal_output=dict(nohash=True,
),
thres=dict(argstr='-threshold %f',
),
)
inputs = EstimateFOD.input_spec()
for key, metadata in input_map.items():
for metakey, value in metadata.items():
yield assert_equal, getattr(inputs.traits()[key], metakey), value
def test_EstimateFOD_outputs():
output_map = dict(out_file=dict(),
)
outputs = EstimateFOD.output_spec()
for key, metadata in output_map.items():
for metakey, value in metadata.items():
yield assert_equal, getattr(outputs.traits()[key], metakey), value
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 fod_pipeline(self, **name_maps): # @UnusedVariable
"""
Estimates the fibre orientation distribution (FOD) using constrained
spherical deconvolution
Parameters
----------
"""
pipeline = self.new_pipeline(
name='fod',
desc=("Estimates the fibre orientation distribution in each"
" voxel"),
citations=[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(algorithm=self.parameter('fod_algorithm')),
inputs={
'in_file': (self.series_preproc_spec_name, nifti_gz_format),
'wm_txt': ('wm_response', text_format),
'mask_file': (self.brain_mask_spec_name, nifti_gz_format),
'grad_fsl': self.fsl_grads(pipeline)
},
outputs={'wm_odf': ('wm_odf', nifti_gz_format)},
requirements=[mrtrix_req.v('3.0rc3')])
if self.multi_tissue:
dwi2fod.inputs.gm_odf = 'gm.mif',
dwi2fod.inputs.csf_odf = 'csf.mif',
pipeline.connect_input('gm_response', dwi2fod, 'gm_txt',
text_format),
pipeline.connect_input('csf_response', dwi2fod, 'csf_txt',
text_format),
pipeline.connect_output('gm_odf', dwi2fod, 'gm_odf',
nifti_gz_format),
pipeline.connect_output('csf_odf', dwi2fod, 'csf_odf',
nifti_gz_format),
# Check inputs/output are connected
return pipeline
def fod_pipeline(self, **kwargs): # @UnusedVariable
"""
Estimates the fibre orientation distribution (FOD) using constrained
spherical deconvolution
Parameters
----------
"""
inputs = [
DatasetSpec('bias_correct', nifti_gz_format),
DatasetSpec('grad_dirs', fsl_bvecs_format),
DatasetSpec('bvalues', fsl_bvals_format),
DatasetSpec('wm_response', text_format),
DatasetSpec('brain_mask', nifti_gz_format)
]
outputs = [DatasetSpec('wm_odf', mrtrix_format)]
if self.multi_tissue:
inputs.append(DatasetSpec('gm_response', text_format))
inputs.append(DatasetSpec('csf_response', text_format))
outputs.append(DatasetSpec('gm_odf', mrtrix_format))
outputs.append(DatasetSpec('csf_odf', mrtrix_format))
algorithm = 'msmt_csd'
else:
algorithm = 'csd'
pipeline = self.create_pipeline(
name='fod',
inputs=inputs,
outputs=outputs,
desc=("Estimates the fibre orientation distribution in each"
" voxel"),
version=1,
citations=[mrtrix_cite],
**kwargs)
# Create fod fit node
dwi2fod = pipeline.create_node(EstimateFOD(),
name='dwi2fod',
requirements=[mrtrix3_req])
dwi2fod.inputs.algorithm = algorithm
# Gradient merge node
fsl_grads = pipeline.create_node(MergeTuple(2), name="fsl_grads")
# 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
