def tensor_pipeline(self, **kwargs): # @UnusedVariable
"""
Fits the apparrent diffusion tensor (DT) to each voxel of the image
"""
pipeline = self.create_pipeline(
name='tensor',
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=[DatasetSpec('tensor', nifti_gz_format)],
desc=("Estimates the apparent diffusion tensor in each "
"voxel"),
version=1,
citations=[],
**kwargs)
# Create tensor fit node
dwi2tensor = pipeline.create_node(FitTensor(), name='dwi2tensor')
dwi2tensor.inputs.out_file = 'dti.nii.gz'
# Gradient merge node
fsl_grads = pipeline.create_node(MergeTuple(2), name="fsl_grads")
# 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 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 tensor_pipeline(self, **name_maps):
"""
Fits the apparrent diffusion tensor (DT) to each voxel of the image
"""
pipeline = self.new_pipeline(
name='tensor',
desc=("Estimates the apparent diffusion tensor in each "
"voxel"),
citations=[],
name_maps=name_maps)
# Create tensor fit node
pipeline.add(
'dwi2tensor',
FitTensor(
out_file='dti.nii.gz'),
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={
'tensor': ('out_file', nifti_gz_format)},
requirements=[mrtrix_req.v('3.0rc3')])
return pipeline
