workflows.dmri.fsl.dti

bedpostx_parallel()

Link to code

Does the same as create_bedpostx_pipeline() by splitting the input dMRI in small ROIs that are better suited for parallel processing).

Example

>>> from nipype.workflows.dmri.fsl.dti import bedpostx_parallel
>>> params = dict(n_fibres = 2, fudge = 1, burn_in = 1000,
...               n_jumps = 1250, sample_every = 25)
>>> bpwf = bedpostx_parallel('nipype_bedpostx_parallel', params=params)
>>> bpwf.inputs.inputnode.dwi = 'diffusion.nii'
>>> bpwf.inputs.inputnode.mask = 'mask.nii'
>>> bpwf.inputs.inputnode.bvecs = 'bvecs'
>>> bpwf.inputs.inputnode.bvals = 'bvals'
>>> bpwf.run(plugin='CondorDAGMan')

Inputs:

inputnode.dwi
inputnode.mask
inputnode.bvecs
inputnode.bvals

Outputs:

outputnode wraps all XFibres outputs

Graph

digraph bedpostx_parallel{ label="bedpostx_parallel"; bedpostx_parallel_inputnode[label="inputnode (utility)"]; bedpostx_parallel_slice_dwi[label="slice_dwi (misc)"]; bedpostx_parallel_xfibres[label="xfibres (fsl)"]; bedpostx_parallel_Merge_dyads[label="Merge_dyads (misc)"]; bedpostx_parallel_Merge_mean_fsamples[label="Merge_mean_fsamples (misc)"]; bedpostx_parallel_Make_dyads[label="Make_dyads (fsl)"]; bedpostx_parallel_outputnode[label="outputnode (utility)"]; bedpostx_parallel_inputnode -> bedpostx_parallel_Merge_dyads; bedpostx_parallel_inputnode -> bedpostx_parallel_Merge_mean_fsamples; bedpostx_parallel_inputnode -> bedpostx_parallel_xfibres; bedpostx_parallel_inputnode -> bedpostx_parallel_xfibres; bedpostx_parallel_inputnode -> bedpostx_parallel_Make_dyads; bedpostx_parallel_inputnode -> bedpostx_parallel_slice_dwi; bedpostx_parallel_inputnode -> bedpostx_parallel_slice_dwi; bedpostx_parallel_slice_dwi -> bedpostx_parallel_Merge_dyads; bedpostx_parallel_slice_dwi -> bedpostx_parallel_Merge_mean_fsamples; bedpostx_parallel_slice_dwi -> bedpostx_parallel_xfibres; bedpostx_parallel_slice_dwi -> bedpostx_parallel_xfibres; bedpostx_parallel_xfibres -> bedpostx_parallel_Merge_dyads; bedpostx_parallel_xfibres -> bedpostx_parallel_Merge_mean_fsamples; bedpostx_parallel_Merge_dyads -> bedpostx_parallel_outputnode; bedpostx_parallel_Merge_mean_fsamples -> bedpostx_parallel_outputnode; subgraph cluster_bedpostx_parallel_thsamples { label="thsamples"; bedpostx_parallel_thsamples_inputnode[label="inputnode (utility)"]; bedpostx_parallel_thsamples_Merge[label="Merge (misc)"]; bedpostx_parallel_thsamples_Mean[label="Mean (fsl)"]; bedpostx_parallel_thsamples_outputnode[label="outputnode (utility)"]; bedpostx_parallel_thsamples_inputnode -> bedpostx_parallel_thsamples_Merge; bedpostx_parallel_thsamples_inputnode -> bedpostx_parallel_thsamples_Merge; bedpostx_parallel_thsamples_inputnode -> bedpostx_parallel_thsamples_Merge; bedpostx_parallel_thsamples_Merge -> bedpostx_parallel_thsamples_Mean; bedpostx_parallel_thsamples_Merge -> bedpostx_parallel_thsamples_outputnode; bedpostx_parallel_thsamples_Mean -> bedpostx_parallel_thsamples_outputnode; } subgraph cluster_bedpostx_parallel_phsamples { label="phsamples"; bedpostx_parallel_phsamples_inputnode[label="inputnode (utility)"]; bedpostx_parallel_phsamples_Merge[label="Merge (misc)"]; bedpostx_parallel_phsamples_Mean[label="Mean (fsl)"]; bedpostx_parallel_phsamples_outputnode[label="outputnode (utility)"]; bedpostx_parallel_phsamples_inputnode -> bedpostx_parallel_phsamples_Merge; bedpostx_parallel_phsamples_inputnode -> bedpostx_parallel_phsamples_Merge; bedpostx_parallel_phsamples_inputnode -> bedpostx_parallel_phsamples_Merge; bedpostx_parallel_phsamples_Merge -> bedpostx_parallel_phsamples_Mean; bedpostx_parallel_phsamples_Merge -> bedpostx_parallel_phsamples_outputnode; bedpostx_parallel_phsamples_Mean -> bedpostx_parallel_phsamples_outputnode; } bedpostx_parallel_Make_dyads -> bedpostx_parallel_outputnode; subgraph cluster_bedpostx_parallel_fsamples { label="fsamples"; bedpostx_parallel_fsamples_inputnode[label="inputnode (utility)"]; bedpostx_parallel_fsamples_Merge[label="Merge (misc)"]; bedpostx_parallel_fsamples_Mean[label="Mean (fsl)"]; bedpostx_parallel_fsamples_outputnode[label="outputnode (utility)"]; bedpostx_parallel_fsamples_inputnode -> bedpostx_parallel_fsamples_Merge; bedpostx_parallel_fsamples_inputnode -> bedpostx_parallel_fsamples_Merge; bedpostx_parallel_fsamples_inputnode -> bedpostx_parallel_fsamples_Merge; bedpostx_parallel_fsamples_Merge -> bedpostx_parallel_fsamples_Mean; bedpostx_parallel_fsamples_Merge -> bedpostx_parallel_fsamples_outputnode; bedpostx_parallel_fsamples_Mean -> bedpostx_parallel_fsamples_outputnode; } bedpostx_parallel_inputnode -> bedpostx_parallel_thsamples_inputnode; bedpostx_parallel_inputnode -> bedpostx_parallel_fsamples_inputnode; bedpostx_parallel_inputnode -> bedpostx_parallel_phsamples_inputnode; bedpostx_parallel_xfibres -> bedpostx_parallel_fsamples_inputnode; bedpostx_parallel_xfibres -> bedpostx_parallel_phsamples_inputnode; bedpostx_parallel_xfibres -> bedpostx_parallel_thsamples_inputnode; bedpostx_parallel_thsamples_outputnode -> bedpostx_parallel_outputnode; bedpostx_parallel_thsamples_outputnode -> bedpostx_parallel_outputnode; bedpostx_parallel_thsamples_outputnode -> bedpostx_parallel_Make_dyads; bedpostx_parallel_fsamples_outputnode -> bedpostx_parallel_outputnode; bedpostx_parallel_fsamples_outputnode -> bedpostx_parallel_outputnode; bedpostx_parallel_phsamples_outputnode -> bedpostx_parallel_outputnode; bedpostx_parallel_phsamples_outputnode -> bedpostx_parallel_outputnode; bedpostx_parallel_phsamples_outputnode -> bedpostx_parallel_Make_dyads; bedpostx_parallel_slice_dwi -> bedpostx_parallel_thsamples_inputnode; bedpostx_parallel_slice_dwi -> bedpostx_parallel_phsamples_inputnode; bedpostx_parallel_slice_dwi -> bedpostx_parallel_fsamples_inputnode; }

create_bedpostx_pipeline()

Link to code

Creates a pipeline that does the same as bedpostx script from FSL - calculates diffusion model parameters (distributions not MLE) voxelwise for the whole volume (by splitting it slicewise).

Example

>>> from nipype.workflows.dmri.fsl.dti import create_bedpostx_pipeline
>>> params = dict(n_fibres = 2, fudge = 1, burn_in = 1000,
...               n_jumps = 1250, sample_every = 25)
>>> bpwf = create_bedpostx_pipeline('nipype_bedpostx', params)
>>> bpwf.inputs.inputnode.dwi = 'diffusion.nii'
>>> bpwf.inputs.inputnode.mask = 'mask.nii'
>>> bpwf.inputs.inputnode.bvecs = 'bvecs'
>>> bpwf.inputs.inputnode.bvals = 'bvals'
>>> bpwf.run()

Inputs:

inputnode.dwi
inputnode.mask
inputnode.bvecs
inputnode.bvals

Outputs:

outputnode wraps all XFibres outputs

Graph

digraph bedpostx{ label="bedpostx"; bedpostx_inputnode[label="inputnode (utility)"]; bedpostx_slice_dwi[label="slice_dwi (fsl)"]; bedpostx_slice_msk[label="slice_msk (fsl)"]; bedpostx_mask_dwi[label="mask_dwi (fsl)"]; bedpostx_xfibres[label="xfibres (fsl)"]; bedpostx_make_dyads[label="make_dyads (fsl)"]; bedpostx_outputnode[label="outputnode (utility)"]; bedpostx_inputnode -> bedpostx_slice_msk; bedpostx_inputnode -> bedpostx_make_dyads; bedpostx_inputnode -> bedpostx_slice_dwi; bedpostx_inputnode -> bedpostx_xfibres; bedpostx_inputnode -> bedpostx_xfibres; bedpostx_slice_dwi -> bedpostx_xfibres; bedpostx_slice_dwi -> bedpostx_mask_dwi; bedpostx_slice_msk -> bedpostx_mask_dwi; bedpostx_mask_dwi -> bedpostx_xfibres; subgraph cluster_bedpostx_phsamples { label="phsamples"; bedpostx_phsamples_inputnode[label="inputnode (utility)"]; bedpostx_phsamples_Merge[label="Merge (fsl)"]; bedpostx_phsamples_Mean[label="Mean (fsl)"]; bedpostx_phsamples_outputnode[label="outputnode (utility)"]; bedpostx_phsamples_inputnode -> bedpostx_phsamples_Merge; bedpostx_phsamples_Merge -> bedpostx_phsamples_outputnode; bedpostx_phsamples_Merge -> bedpostx_phsamples_Mean; bedpostx_phsamples_Mean -> bedpostx_phsamples_outputnode; } subgraph cluster_bedpostx_thsamples { label="thsamples"; bedpostx_thsamples_inputnode[label="inputnode (utility)"]; bedpostx_thsamples_Merge[label="Merge (fsl)"]; bedpostx_thsamples_Mean[label="Mean (fsl)"]; bedpostx_thsamples_outputnode[label="outputnode (utility)"]; bedpostx_thsamples_inputnode -> bedpostx_thsamples_Merge; bedpostx_thsamples_Merge -> bedpostx_thsamples_Mean; bedpostx_thsamples_Merge -> bedpostx_thsamples_outputnode; bedpostx_thsamples_Mean -> bedpostx_thsamples_outputnode; } bedpostx_make_dyads -> bedpostx_outputnode; bedpostx_make_dyads -> bedpostx_outputnode; subgraph cluster_bedpostx_fsamples { label="fsamples"; bedpostx_fsamples_inputnode[label="inputnode (utility)"]; bedpostx_fsamples_Merge[label="Merge (fsl)"]; bedpostx_fsamples_Mean[label="Mean (fsl)"]; bedpostx_fsamples_outputnode[label="outputnode (utility)"]; bedpostx_fsamples_inputnode -> bedpostx_fsamples_Merge; bedpostx_fsamples_Merge -> bedpostx_fsamples_Mean; bedpostx_fsamples_Merge -> bedpostx_fsamples_outputnode; bedpostx_fsamples_Mean -> bedpostx_fsamples_outputnode; } bedpostx_xfibres -> bedpostx_phsamples_inputnode; bedpostx_xfibres -> bedpostx_thsamples_inputnode; bedpostx_xfibres -> bedpostx_fsamples_inputnode; bedpostx_thsamples_outputnode -> bedpostx_outputnode; bedpostx_thsamples_outputnode -> bedpostx_outputnode; bedpostx_thsamples_outputnode -> bedpostx_make_dyads; bedpostx_fsamples_outputnode -> bedpostx_outputnode; bedpostx_fsamples_outputnode -> bedpostx_outputnode; bedpostx_phsamples_outputnode -> bedpostx_outputnode; bedpostx_phsamples_outputnode -> bedpostx_outputnode; bedpostx_phsamples_outputnode -> bedpostx_make_dyads; }

merge_and_mean()

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Graph

digraph mm{ label="mm"; mm_inputnode[label="inputnode (utility)"]; mm_Merge[label="Merge (fsl)"]; mm_Mean[label="Mean (fsl)"]; mm_outputnode[label="outputnode (utility)"]; mm_inputnode -> mm_Merge; mm_Merge -> mm_Mean; mm_Merge -> mm_outputnode; mm_Mean -> mm_outputnode; }

merge_and_mean_parallel()

Link to code

Graph

digraph mm{ label="mm"; mm_inputnode[label="inputnode (utility)"]; mm_Merge[label="Merge (misc)"]; mm_Mean[label="Mean (fsl)"]; mm_outputnode[label="outputnode (utility)"]; mm_inputnode -> mm_Merge; mm_inputnode -> mm_Merge; mm_inputnode -> mm_Merge; mm_Merge -> mm_Mean; mm_Merge -> mm_outputnode; mm_Mean -> mm_outputnode; }

transpose()

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