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josephmjeoesteban
josephmje
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oesteban
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joining gradtable and skullstrip
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‎dmriprep/config/__init__.py

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# vi: set ft=python sts=4 ts=4 sw=4 et:
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"""Settings."""
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DEFAULT_MEMORY_MIN_GB = 0.01
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NONSTANDARD_REFERENCES = ['anat', 'T1w', 'dwi', 'fsnative']

‎dmriprep/config/reports-spec.yml

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caption: Surfaces (white and pial) reconstructed with FreeSurfer (<code>recon-all</code>)
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overlaid on the participant's T1w template.
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subtitle: Surface reconstruction
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- name: Diffusion
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ordering: session, acq, run
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reportlets:
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- bids: {datatype: dwi, desc: summary, suffix: dwi}
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- bids: {datatype: dwi, desc: validation, suffix: dwi}
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- bids: {datatype: dwi, suffix: 'dwi'}
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caption: Brain mask calculated on the dwi signal (red contour).
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subtitle: Brain mask
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- name: About
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reportlets:
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- bids: {datatype: anat, desc: about, suffix: T1w}

‎dmriprep/workflows/base.py

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from ..interfaces.reports import SubjectSummary, AboutSummary
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from ..utils.bids import collect_data
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from ..__about__ import __version__
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# from .dwi import init_dwi_preproc_wf
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from .dwi import init_dwi_preproc_wf
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def init_dmriprep_wf(
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if anat_only:
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return workflow
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# for dwi_file in subject_data['dwi']:
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# dwi_preproc_wf = init_dwi_preproc_wf(
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# aroma_melodic_dim=aroma_melodic_dim,
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# bold2t1w_dof=bold2t1w_dof,
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# bold_file=bold_file,
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# cifti_output=cifti_output,
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# debug=debug,
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# dummy_scans=dummy_scans,
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# err_on_aroma_warn=err_on_aroma_warn,
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# fmap_bspline=fmap_bspline,
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# fmap_demean=fmap_demean,
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# force_syn=force_syn,
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# freesurfer=freesurfer,
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# ignore=ignore,
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# layout=layout,
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# low_mem=low_mem,
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# medial_surface_nan=medial_surface_nan,
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# num_bold=len(subject_data['bold']),
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# omp_nthreads=omp_nthreads,
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# output_dir=output_dir,
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# output_spaces=output_spaces,
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# reportlets_dir=reportlets_dir,
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# regressors_all_comps=regressors_all_comps,
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# regressors_fd_th=regressors_fd_th,
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# regressors_dvars_th=regressors_dvars_th,
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# t2s_coreg=t2s_coreg,
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# use_aroma=use_aroma,
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# use_syn=use_syn,
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# )
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for dwi_file in subject_data['dwi']:
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dwi_preproc_wf = init_dwi_preproc_wf(
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dwi_file=dwi_file,
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debug=debug,
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force_syn=force_syn,
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ignore=ignore,
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layout=layout,
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low_mem=low_mem,
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num_dwi=len(subject_data['dwi']),
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omp_nthreads=omp_nthreads,
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output_dir=output_dir,
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reportlets_dir=reportlets_dir,
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use_syn=use_syn,
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)
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# workflow.connect([
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# (anat_preproc_wf, dwi_preproc_wf,

‎dmriprep/workflows/dwi/__init__.py

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"""
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Pre-processing dMRI workflows
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.. automodule:: dmriprep.workflows.dwi.base
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.. automodule:: dmriprep.workflows.dwi.util
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"""
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from .base import init_dwi_preproc_wf
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from .util import init_dwi_reference_wf
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_all__ = [
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'init_dwi_preproc_wf',
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'init_dwi_reference_wf'
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]

‎dmriprep/workflows/dwi/base.py

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"""
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Orchestrating the dMRI-preprocessing workflow.
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.. autofunction:: init_dwi_preproc_wf
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.. autofunction:: init_dwi_derivatives_wf
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"""
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from nipype import logging
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from nipype.pipeline import engine as pe
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from nipype.interfaces import utility as niu
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from niworkflows.engine.workflows import LiterateWorkflow as Workflow
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from ...config import DEFAULT_MEMORY_MIN_GB
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from ...interfaces import DerivativesDataSink
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from ...interfaces.reports import DiffusionSummary
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from ...interfaces.vectors import CheckGradientTable
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# dwi workflows
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from .util import init_dwi_reference_wf
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LOGGING = logging.getLogger('nipype.workflow')
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def init_dwi_preproc_wf(
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dwi_file,
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debug,
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force_syn,
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ignore,
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low_mem,
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omp_nthreads,
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output_dir,
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reportlets_dir,
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use_syn,
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layout=None,
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num_dwi=1,
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):
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"""
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This workflow controls the diffusion preprocessing stages of *dMRIPrep*.
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Workflow Graph:
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.. workflow::
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:graph2use: orig
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:simple_form: yes
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from dmriprep.workflows.dwi import init_dwi_preproc_wf
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from collections import namedtuple
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BIDSLayout = namedtuple('BIDSLayout', ['root'])
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wf = init_dwi_preproc_wf(
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dwi_file='/completely/made/up/path/sub-01_dwi.nii.gz',
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debug=False,
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force_syn=True,
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ignore=[],
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low_mem=False,
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omp_nthreads=1,
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output_dir='.',
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reportlets_dir='.',
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layout=BIDSLayout('.'),
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num_dwi=1,
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)
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Parameters
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----------
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dwi_file : str
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dwi NIfTI file
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debug : bool
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Enable debugging outputs
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force_syn : bool
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**Temporary**: Always run SyN-based SDC
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ignore : list
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Preprocessing steps to skip (may include "sdc")
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low_mem : bool
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Write uncompressed .nii files in some cases to reduce memory usage
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omp_nthreads : int
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Maximum number of threads an individual process may use
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output_dir : str
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Directory in which to save derivatives
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reportlets_dir : str
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Absolute path of a directory in which reportlets will be temporarily stored
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use_syn : bool
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**Experimental**: Enable ANTs SyN-based susceptibility distortion correction (SDC).
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If fieldmaps are present and enabled, this is not run, by default.
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layout : BIDSLayout
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BIDSLayout structure to enable metadata retrieval
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num_dwi : int
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Total number of dwi files that have been set for preprocessing
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(default is 1)
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Inputs
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------
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dwi_file
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dwi NIfTI file
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bvec_file
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File path of the b-values
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bval_file
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File path of the b-vectors
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Outputs
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-------
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dwi_file
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dwi NIfTI file
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dwi_mask
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dwi mask
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See also
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--------
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* :py_func:`~dmriprep.workflows.dwi.util.init_dwi_reference_wf
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"""
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wf_name = _get_wf_name(dwi_file)
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# Build workflow
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workflow = Workflow(name=wf_name)
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workflow.__desc__ = """
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Diffusion data preprocessing
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: For each of the {num_dwi} dwi scans found per subject (across all sessions),
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the following preprocessing was performed.
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""".format(num_dwi=num_dwi)
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workflow.__postdesc__ = """\
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"""
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metadata = layout.get_metadata(dwi_file)
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inputnode = pe.Node(niu.IdentityInterface(
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fields=['dwi_file']),
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name='inputnode')
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inputnode.inputs.dwi_file = dwi_file
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inputnode.inputs.bvec_file = layout.get_bvec(dwi_file)
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inputnode.inputs.bval_file = layout.get_bval(dwi_file)
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outputnode = pe.Node(niu.IdentityInterface(
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fields=['dwi_file', 'bvec_file', 'bval_file', 'rasb_file',
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'dwi_mask']),
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name='outputnode')
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summary = pe.Node(
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DiffusionSummary(
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pe_direction=metadata.get("PhaseEncodingDirection")),
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name='summary', mem_gb=DEFAULT_MEMORY_MIN_GB, run_without_submitting=True)
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gradient_table = pe.Node(CheckGradientTable(), name='gradient_table')
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dwi_reference_wf = init_dwi_reference_wf(omp_nthreads=1, gen_report=True)
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# dwi_derivatives_wf = init_dwi_derivatives_wf()
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# MAIN WORKFLOW STRUCTURE
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workflow.connect([
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(inputnode, gradient_table, [
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('dwi_file', 'dwi_file'),
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('bvec_file', 'in_bvec'),
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('bval_file', 'in_bval')]),
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(inputnode, dwi_reference_wf, [('dwi_file', 'inputnode.dwi_file')]),
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(gradient_table, dwi_reference_wf, [('b0_ixs', 'inputnode.b0_ixs')]),
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])
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# REPORTING
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ds_report_summary = pe.Node(
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DerivativesDataSink(desc='summary', keep_dtype=True),
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name='ds_report_summary', run_without_submitting=True,
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mem_gb=DEFAULT_MEMORY_MIN_GB
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)
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return workflow
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def _get_wf_name(dwi_fname):
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"""
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Derive the workflow name for supplied dwi file.
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>>> _get_wf_name('/completely/made/up/path/sub-01_dwi.nii.gz')
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'dwi_preproc_wf'
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>>> _get_wf_name('/completely/made/up/path/sub-01_run-1_dwi.nii.gz')
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'dwi_preproc_run_1_wf'
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"""
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from nipype.utils.filemanip import split_filename
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fname = split_filename(dwi_fname)[1]
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fname_nosub = '_'.join(fname.split("_")[1:])
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name = "dwi_preproc_" + fname_nosub.replace(
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".", "_").replace(" ", "").replace("-", "_").replace("_dwi", "_wf")
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return name

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