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add new files for EPI preprocessing
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‎nipype/workflows/preprocessing/epi/__init__.py

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# coding: utf-8
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# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*-
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# vi: set ft=python sts=4 ts=4 sw=4 et:
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from bias import remove_bias
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from eddy import ecc_fsl
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from susceptibility import sdc_fmb, sdc_peb
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from motion import hmc_flirt
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from fsl import all_dmri
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from complete import all_fmb_pipeline, all_peb_pipeline

‎nipype/workflows/preprocessing/epi/bias.py

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# coding: utf-8
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# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*-
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# vi: set ft=python sts=4 ts=4 sw=4 et:
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import os
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import nipype.pipeline.engine as pe
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from nipype.interfaces.io import JSONFileGrabber
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from nipype.interfaces import utility as niu
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from nipype.interfaces import freesurfer as fs
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from nipype.interfaces import ants
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from nipype.interfaces import fsl
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from .utils import *
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def remove_bias(name='bias_correct'):
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"""
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This workflow estimates a single multiplicative bias field from the
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averaged *b0* image, as suggested in [Jeurissen2014]_.
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.. admonition:: References
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.. [Jeurissen2014] Jeurissen B. et al., `Multi-tissue constrained
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spherical deconvolution for improved analysis of multi-shell diffusion
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MRI data <http://dx.doi.org/10.1016/j.neuroimage.2014.07.061>`_.
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NeuroImage (2014). doi: 10.1016/j.neuroimage.2014.07.061
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Example
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-------
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>>> from nipype.workflows.dmri.fsl.artifacts import remove_bias
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>>> bias = remove_bias()
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>>> bias.inputs.inputnode.in_file = 'epi.nii'
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>>> bias.inputs.inputnode.in_bval = 'diffusion.bval'
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>>> bias.inputs.inputnode.in_mask = 'mask.nii'
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>>> bias.run() # doctest: +SKIP
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"""
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inputnode = pe.Node(niu.IdentityInterface(
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fields=['in_file', 'in_bval', 'in_mask']), name='inputnode')
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outputnode = pe.Node(niu.IdentityInterface(fields=['out_file']),
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name='outputnode')
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avg_b0 = pe.Node(niu.Function(
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input_names=['in_dwi', 'in_bval'], output_names=['out_file'],
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function=b0_average), name='b0_avg')
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n4 = pe.Node(ants.N4BiasFieldCorrection(
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dimension=3, save_bias=True, bspline_fitting_distance=600),
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name='Bias_b0')
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split = pe.Node(fsl.Split(dimension='t'), name='SplitDWIs')
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mult = pe.MapNode(fsl.MultiImageMaths(op_string='-div %s'),
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iterfield=['in_file'], name='RemoveBiasOfDWIs')
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thres = pe.MapNode(fsl.Threshold(thresh=0.0), iterfield=['in_file'],
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name='RemoveNegative')
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merge = pe.Node(fsl.utils.Merge(dimension='t'), name='MergeDWIs')
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wf = pe.Workflow(name=name)
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wf.connect([
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(inputnode, avg_b0, [('in_file', 'in_dwi'),
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('in_bval', 'in_bval')]),
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(avg_b0, n4, [('out_file', 'input_image')]),
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(inputnode, n4, [('in_mask', 'mask_image')]),
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(inputnode, split, [('in_file', 'in_file')]),
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(n4, mult, [('bias_image', 'operand_files')]),
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(split, mult, [('out_files', 'in_file')]),
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(mult, thres, [('out_file', 'in_file')]),
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(thres, merge, [('out_file', 'in_files')]),
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(merge, outputnode, [('merged_file', 'out_file')])
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])
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return wf

‎nipype/workflows/preprocessing/epi/complete.py

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# coding: utf-8
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# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*-
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# vi: set ft=python sts=4 ts=4 sw=4 et:
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import os
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import nipype.pipeline.engine as pe
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from nipype.interfaces.io import JSONFileGrabber
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from nipype.interfaces import utility as niu
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from nipype.interfaces import freesurfer as fs
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from nipype.interfaces import ants
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from nipype.interfaces import fsl
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from .utils import *
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def all_fmb_pipeline(name='hmc_sdc_ecc', fugue_params=dict(smooth3d=2.0)):
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"""
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Builds a pipeline including three artifact corrections: head-motion
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correction (HMC), susceptibility-derived distortion correction (SDC),
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and Eddy currents-derived distortion correction (ECC).
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The displacement fields from each kind of distortions are combined. Thus,
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only one interpolation occurs between input data and result.
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.. warning:: this workflow rotates the gradients table (*b*-vectors)
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[Leemans09]_.
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Examples
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--------
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>>> from nipype.workflows.dmri.fsl.artifacts import all_fmb_pipeline
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>>> allcorr = all_fmb_pipeline()
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>>> allcorr.inputs.inputnode.in_file = 'epi.nii'
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>>> allcorr.inputs.inputnode.in_bval = 'diffusion.bval'
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>>> allcorr.inputs.inputnode.in_bvec = 'diffusion.bvec'
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>>> allcorr.inputs.inputnode.bmap_mag = 'magnitude.nii'
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>>> allcorr.inputs.inputnode.bmap_pha = 'phase.nii'
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>>> allcorr.inputs.inputnode.epi_param = 'epi_param.txt'
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>>> allcorr.run() # doctest: +SKIP
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"""
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inputnode = pe.Node(niu.IdentityInterface(
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fields=['in_file', 'in_bvec', 'in_bval', 'bmap_pha', 'bmap_mag',
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'epi_param']), name='inputnode')
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outputnode = pe.Node(niu.IdentityInterface(
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fields=['out_file', 'out_mask', 'out_bvec']), name='outputnode')
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list_b0 = pe.Node(niu.Function(
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input_names=['in_bval'], output_names=['out_idx'],
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function=b0_indices), name='B0indices')
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avg_b0_0 = pe.Node(niu.Function(
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input_names=['in_file', 'index'], output_names=['out_file'],
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function=time_avg), name='b0_avg_pre')
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avg_b0_1 = pe.Node(niu.Function(
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input_names=['in_file', 'index'], output_names=['out_file'],
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function=time_avg), name='b0_avg_post')
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bet_dwi0 = pe.Node(fsl.BET(frac=0.3, mask=True, robust=True),
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name='bet_dwi_pre')
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bet_dwi1 = pe.Node(fsl.BET(frac=0.3, mask=True, robust=True),
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name='bet_dwi_post')
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hmc = hmc_pipeline()
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sdc = sdc_fmb(fugue_params=fugue_params)
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ecc = ecc_pipeline()
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unwarp = apply_all_corrections()
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wf = pe.Workflow(name=name)
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wf.connect([
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(inputnode, hmc, [('in_file', 'inputnode.in_file'),
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('in_bvec', 'inputnode.in_bvec'),
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('in_bval', 'inputnode.in_bval')]),
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(inputnode, list_b0, [('in_bval', 'in_bval')]),
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(inputnode, avg_b0_0, [('in_file', 'in_file')]),
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(list_b0, avg_b0_0, [('out_idx', 'index')]),
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(avg_b0_0, bet_dwi0, [('out_file', 'in_file')]),
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(bet_dwi0, hmc, [('mask_file', 'inputnode.in_mask')]),
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(hmc, sdc, [
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('outputnode.out_file', 'inputnode.in_file')]),
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(bet_dwi0, sdc, [('mask_file', 'inputnode.in_mask')]),
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(inputnode, sdc, [('bmap_pha', 'inputnode.bmap_pha'),
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('bmap_mag', 'inputnode.bmap_mag'),
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('epi_param', 'inputnode.settings')]),
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(list_b0, sdc, [('out_idx', 'inputnode.in_ref')]),
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(hmc, ecc, [
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('outputnode.out_xfms', 'inputnode.in_xfms')]),
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(inputnode, ecc, [('in_file', 'inputnode.in_file'),
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('in_bval', 'inputnode.in_bval')]),
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(bet_dwi0, ecc, [('mask_file', 'inputnode.in_mask')]),
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(ecc, avg_b0_1, [('outputnode.out_file', 'in_file')]),
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(list_b0, avg_b0_1, [('out_idx', 'index')]),
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(avg_b0_1, bet_dwi1, [('out_file', 'in_file')]),
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(inputnode, unwarp, [('in_file', 'inputnode.in_dwi')]),
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(hmc, unwarp, [('outputnode.out_xfms', 'inputnode.in_hmc')]),
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(ecc, unwarp, [('outputnode.out_xfms', 'inputnode.in_ecc')]),
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(sdc, unwarp, [('outputnode.out_warp', 'inputnode.in_sdc')]),
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(hmc, outputnode, [('outputnode.out_bvec', 'out_bvec')]),
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(unwarp, outputnode, [('outputnode.out_file', 'out_file')]),
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(bet_dwi1, outputnode, [('mask_file', 'out_mask')])
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])
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return wf
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def all_peb_pipeline(name='hmc_sdc_ecc',
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epi_params=dict(echospacing=0.77e-3,
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acc_factor=3,
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enc_dir='y-',
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epi_factor=1),
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altepi_params=dict(echospacing=0.77e-3,
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acc_factor=3,
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enc_dir='y',
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epi_factor=1)):
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"""
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Builds a pipeline including three artifact corrections: head-motion
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correction (HMC), susceptibility-derived distortion correction (SDC),
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and Eddy currents-derived distortion correction (ECC).
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.. warning:: this workflow rotates the gradients table (*b*-vectors)
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[Leemans09]_.
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Examples
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--------
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>>> from nipype.workflows.dmri.fsl.artifacts import all_peb_pipeline
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>>> allcorr = all_peb_pipeline()
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>>> allcorr.inputs.inputnode.in_file = 'epi.nii'
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>>> allcorr.inputs.inputnode.alt_file = 'epi_rev.nii'
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>>> allcorr.inputs.inputnode.in_bval = 'diffusion.bval'
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>>> allcorr.inputs.inputnode.in_bvec = 'diffusion.bvec'
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>>> allcorr.run() # doctest: +SKIP
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"""
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inputnode = pe.Node(niu.IdentityInterface(
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fields=['in_file', 'in_bvec', 'in_bval', 'alt_file']),
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name='inputnode')
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outputnode = pe.Node(niu.IdentityInterface(
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fields=['out_file', 'out_mask', 'out_bvec']), name='outputnode')
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avg_b0_0 = pe.Node(niu.Function(
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input_names=['in_dwi', 'in_bval'], output_names=['out_file'],
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function=b0_average), name='b0_avg_pre')
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avg_b0_1 = pe.Node(niu.Function(
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input_names=['in_dwi', 'in_bval'], output_names=['out_file'],
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function=b0_average), name='b0_avg_post')
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bet_dwi0 = pe.Node(fsl.BET(frac=0.3, mask=True, robust=True),
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name='bet_dwi_pre')
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bet_dwi1 = pe.Node(fsl.BET(frac=0.3, mask=True, robust=True),
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name='bet_dwi_post')
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hmc = hmc_pipeline()
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sdc = sdc_peb(epi_params=epi_params, altepi_params=altepi_params)
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ecc = ecc_pipeline()
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unwarp = apply_all_corrections()
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wf = pe.Workflow(name=name)
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wf.connect([
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(inputnode, hmc, [('in_file', 'inputnode.in_file'),
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('in_bvec', 'inputnode.in_bvec'),
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('in_bval', 'inputnode.in_bval')]),
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(inputnode, avg_b0_0, [('in_file', 'in_dwi'),
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('in_bval', 'in_bval')]),
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(avg_b0_0, bet_dwi0, [('out_file', 'in_file')]),
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(bet_dwi0, hmc, [('mask_file', 'inputnode.in_mask')]),
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(hmc, sdc, [
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('outputnode.out_file', 'inputnode.in_file')]),
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(bet_dwi0, sdc, [('mask_file', 'inputnode.in_mask')]),
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(inputnode, sdc, [('in_bval', 'inputnode.in_bval'),
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('alt_file', 'inputnode.alt_file')]),
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(inputnode, ecc, [('in_file', 'inputnode.in_file'),
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('in_bval', 'inputnode.in_bval')]),
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(bet_dwi0, ecc, [('mask_file', 'inputnode.in_mask')]),
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(hmc, ecc, [
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('outputnode.out_xfms', 'inputnode.in_xfms')]),
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(ecc, avg_b0_1, [('outputnode.out_file', 'in_dwi')]),
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(inputnode, avg_b0_1, [('in_bval', 'in_bval')]),
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(avg_b0_1, bet_dwi1, [('out_file', 'in_file')]),
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(inputnode, unwarp, [('in_file', 'inputnode.in_dwi')]),
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(hmc, unwarp, [('outputnode.out_xfms', 'inputnode.in_hmc')]),
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(ecc, unwarp, [('outputnode.out_xfms', 'inputnode.in_ecc')]),
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(sdc, unwarp, [('outputnode.out_warp', 'inputnode.in_sdc')]),
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(hmc, outputnode, [('outputnode.out_bvec', 'out_bvec')]),
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(unwarp, outputnode, [('outputnode.out_file', 'out_file')]),
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(bet_dwi1, outputnode, [('mask_file', 'out_mask')])
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])
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return wf

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