Source code for mriqc.workflows.functional

# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*-
# vi: set ft=python sts=4 ts=4 sw=4 et:
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Functional workflow

.. image :: _static/functional_workflow_source.svg

The functional workflow follows the following steps:

#. Sanitize (revise data types and xforms) input data, read
   associated metadata and discard non-steady state frames.
#. :abbr:`HMC (head-motion correction)` based on ``3dvolreg`` from
   AFNI -- :py:func:`hmc`.
#. Skull-stripping of the time-series (AFNI) --
#. Calculate mean time-series, and :abbr:`tSNR (temporal SNR)`.
#. Spatial Normalization to MNI (ANTs) -- :py:func:`epi_mni_align`
#. Extraction of IQMs -- :py:func:`compute_iqms`.
#. Individual-reports generation -- :py:func:`individual_reports`.

This workflow is orchestrated by :py:func:`fmri_qc_workflow`.
from mriqc import config
from nipype.interfaces import io as nio
from nipype.interfaces import utility as niu
from nipype.pipeline import engine as pe
from mriqc.interfaces.datalad import DataladIdentityInterface

[docs]def fmri_qc_workflow(name="funcMRIQC"): """ Initialize the (f)MRIQC workflow. .. workflow:: import os.path as op from mriqc.workflows.functional import fmri_qc_workflow from mriqc.testing import mock_config with mock_config(): wf = fmri_qc_workflow() """ from nipype.algorithms.confounds import TSNR, NonSteadyStateDetector from nipype.interfaces.afni import TStat from niworkflows.interfaces.header import SanitizeImage from mriqc.messages import BUILDING_WORKFLOW workflow = pe.Workflow(name=name) mem_gb = config.workflow.biggest_file_gb dataset = config.workflow.inputs.get("bold", []) message = BUILDING_WORKFLOW.format( modality="functional", detail=( f"for {len(dataset)} NIfTI files." if len(dataset) > 2 else f"({' and '.join(('<%s>' % v for v in dataset))})." ), ) # Define workflow, inputs and outputs # 0. Get data, put it in RAS orientation inputnode = pe.Node(niu.IdentityInterface(fields=["in_file"]), name="inputnode") inputnode.iterables = [("in_file", dataset)] datalad_get = pe.Node(DataladIdentityInterface( fields=["in_file"], dataset_path=config.execution.bids_dir ), name="datalad_get") outputnode = pe.Node( niu.IdentityInterface( fields=["qc", "mosaic", "out_group", "out_dvars", "out_fd"] ), name="outputnode", ) non_steady_state_detector = pe.Node( NonSteadyStateDetector(), name="non_steady_state_detector" ) sanitize = pe.Node(SanitizeImage(), name="sanitize", mem_gb=mem_gb * 4.0) sanitize.inputs.max_32bit = config.execution.float32 # Workflow -------------------------------------------------------- # 1. HMC: head motion correct hmcwf = hmc() # Set HMC settings hmcwf.inputs.inputnode.fd_radius = config.workflow.fd_radius # 2. Compute mean fmri mean = pe.Node( TStat(options="-mean", outputtype="NIFTI_GZ"), name="mean", mem_gb=mem_gb * 1.5, ) # EPI to MNI registration ema = epi_mni_align() # Compute TSNR using nipype implementation tsnr = pe.Node(TSNR(), name="compute_tsnr", mem_gb=mem_gb * 2.5) # 7. Compute IQMs iqmswf = compute_iqms() # Reports repwf = individual_reports() # fmt: off workflow.connect([ (inputnode, datalad_get, [("in_file", "in_file")]), (datalad_get, iqmswf, [("in_file", "inputnode.in_file")]), (datalad_get, sanitize, [("in_file", "in_file")]), (datalad_get, non_steady_state_detector, [("in_file", "in_file")]), (non_steady_state_detector, sanitize, [("n_volumes_to_discard", "n_volumes_to_discard")]), (sanitize, hmcwf, [("out_file", "inputnode.in_file")]), (hmcwf, mean, [("outputnode.out_file", "in_file")]), (hmcwf, tsnr, [("outputnode.out_file", "in_file")]), (mean, ema, [("out_file", "inputnode.epi_mean")]), (sanitize, iqmswf, [("out_file", "inputnode.in_ras")]), (mean, iqmswf, [("out_file", "inputnode.epi_mean")]), (hmcwf, iqmswf, [("outputnode.out_file", "inputnode.hmc_epi"), ("outputnode.out_fd", "inputnode.hmc_fd")]), (tsnr, iqmswf, [("tsnr_file", "inputnode.in_tsnr")]), (sanitize, repwf, [("out_file", "inputnode.in_ras")]), (mean, repwf, [("out_file", "inputnode.epi_mean")]), (tsnr, repwf, [("stddev_file", "inputnode.in_stddev")]), (hmcwf, repwf, [("outputnode.out_fd", "inputnode.hmc_fd"), ("outputnode.out_file", "inputnode.hmc_epi")]), (ema, repwf, [("outputnode.epi_parc", "inputnode.epi_parc"), ("", "inputnode.mni_report")]), (non_steady_state_detector, iqmswf, [("n_volumes_to_discard", "inputnode.exclude_index")]), (iqmswf, repwf, [("outputnode.out_file", "inputnode.in_iqms"), ("outputnode.out_dvars", "inputnode.in_dvars"), ("outputnode.outliers", "inputnode.outliers"), ("outputnode.meta_sidecar", "inputnode.meta_sidecar")]), (hmcwf, outputnode, [("outputnode.out_fd", "out_fd")]), ]) # fmt: on if config.workflow.fft_spikes_detector: # fmt: off workflow.connect([ (iqmswf, repwf, [("outputnode.out_spikes", "inputnode.in_spikes"), ("outputnode.out_fft", "inputnode.in_fft")]), ]) # fmt: on if config.workflow.ica: from niworkflows.interfaces.reportlets.segmentation import MELODICRPT melodic = pe.Node( MELODICRPT( no_bet=True, no_mask=True, no_mm=True, compress_report=False, generate_report=True, ), name="ICA", mem_gb=max(mem_gb * 5, 8), ) # fmt: off workflow.connect([ (sanitize, melodic, [("out_file", "in_files")]), (melodic, repwf, [("out_report", "inputnode.ica_report")]) ]) # fmt: on # population specific changes to brain masking if config.workflow.species == "human": skullstrip_epi = fmri_bmsk_workflow() # fmt: off workflow.connect([ (mean, skullstrip_epi, [("out_file", "inputnode.in_file")]), (skullstrip_epi, ema, [("outputnode.out_file", "inputnode.epi_mask")]), (skullstrip_epi, iqmswf, [("outputnode.out_file", "inputnode.brainmask")]), (skullstrip_epi, repwf, [("outputnode.out_file", "inputnode.brainmask")]), ]) # fmt: on if config.workflow.ica: workflow.connect( [(skullstrip_epi, melodic, [("outputnode.out_file", "report_mask")])] ) else: from .anatomical import _binarize binarise_labels = pe.Node( niu.Function( input_names=["in_file", "threshold"], output_names=["out_file"], function=_binarize, ), name="binarise_labels", ) # fmt: off workflow.connect([ (ema, binarise_labels, [("outputnode.epi_parc", "in_file")]), (binarise_labels, iqmswf, [("out_file", "inputnode.brainmask")]), (binarise_labels, repwf, [("out_file", "inputnode.brainmask")]) ]) # fmt: on if config.workflow.ica: workflow.connect( [(binarise_labels, melodic, [("out_file", "report_mask")])] ) # Upload metrics if not config.execution.no_sub: from mriqc.interfaces.webapi import UploadIQMs upldwf = pe.Node(UploadIQMs(), name="UploadMetrics") upldwf.inputs.url = config.execution.webapi_url upldwf.inputs.strict = config.execution.upload_strict if config.execution.webapi_port: upldwf.inputs.port = config.execution.webapi_port # fmt: off workflow.connect([ (iqmswf, upldwf, [("outputnode.out_file", "in_iqms")]), ]) # fmt: on return workflow
[docs]def compute_iqms(name="ComputeIQMs"): """ Initialize the workflow that actually computes the IQMs. .. workflow:: from mriqc.workflows.functional import compute_iqms from mriqc.testing import mock_config with mock_config(): wf = compute_iqms() """ from nipype.algorithms.confounds import ComputeDVARS from nipype.interfaces.afni import OutlierCount, QualityIndex from niworkflows.interfaces.bids import ReadSidecarJSON from mriqc.interfaces import FunctionalQC, IQMFileSink from mriqc.interfaces.reports import AddProvenance from mriqc.interfaces.transitional import GCOR from mriqc.workflows.utils import _tofloat, get_fwhmx mem_gb = config.workflow.biggest_file_gb workflow = pe.Workflow(name=name) inputnode = pe.Node( niu.IdentityInterface( fields=[ "in_file", "in_ras", "epi_mean", "brainmask", "hmc_epi", "hmc_fd", "fd_thres", "in_tsnr", "metadata", "exclude_index", ] ), name="inputnode", ) outputnode = pe.Node( niu.IdentityInterface( fields=[ "out_file", "out_dvars", "outliers", "out_spikes", "out_fft", "meta_sidecar", ] ), name="outputnode", ) # Set FD threshold inputnode.inputs.fd_thres = config.workflow.fd_thres # Compute DVARS dvnode = pe.Node( ComputeDVARS(save_plot=False, save_all=True), name="ComputeDVARS", mem_gb=mem_gb * 3, ) # AFNI quality measures fwhm_interface = get_fwhmx() fwhm = pe.Node(fwhm_interface, name="smoothness") # fwhm.inputs.acf = True # add when AFNI >= 16 outliers = pe.Node( OutlierCount(fraction=True, out_file="outliers.out"), name="outliers", mem_gb=mem_gb * 2.5, ) quality = pe.Node( QualityIndex(automask=True), out_file="quality.out", name="quality", mem_gb=mem_gb * 3, ) gcor = pe.Node(GCOR(), name="gcor", mem_gb=mem_gb * 2) measures = pe.Node(FunctionalQC(), name="measures", mem_gb=mem_gb * 3) # fmt: off workflow.connect([ (inputnode, dvnode, [("hmc_epi", "in_file"), ("brainmask", "in_mask")]), (inputnode, measures, [("epi_mean", "in_epi"), ("brainmask", "in_mask"), ("hmc_epi", "in_hmc"), ("hmc_fd", "in_fd"), ("fd_thres", "fd_thres"), ("in_tsnr", "in_tsnr")]), (inputnode, fwhm, [("epi_mean", "in_file"), ("brainmask", "mask")]), (inputnode, quality, [("hmc_epi", "in_file")]), (inputnode, outliers, [("hmc_epi", "in_file"), ("brainmask", "mask")]), (inputnode, gcor, [("hmc_epi", "in_file"), ("brainmask", "mask")]), (dvnode, measures, [("out_all", "in_dvars")]), (fwhm, measures, [(("fwhm", _tofloat), "in_fwhm")]), (dvnode, outputnode, [("out_all", "out_dvars")]), (outliers, outputnode, [("out_file", "outliers")]) ]) # fmt: on # Add metadata meta = pe.Node(ReadSidecarJSON(), name="metadata") addprov = pe.Node( AddProvenance(modality="bold"), name="provenance", run_without_submitting=True, ) # Save to JSON file datasink = pe.Node( IQMFileSink( modality="bold", out_dir=str(config.execution.output_dir), dataset=config.execution.dsname, ), name="datasink", run_without_submitting=True, ) # fmt: off workflow.connect([ (inputnode, datasink, [("in_file", "in_file"), ("exclude_index", "dummy_trs")]), (inputnode, meta, [("in_file", "in_file")]), (inputnode, addprov, [("in_file", "in_file")]), (meta, datasink, [("subject", "subject_id"), ("session", "session_id"), ("task", "task_id"), ("acquisition", "acq_id"), ("reconstruction", "rec_id"), ("run", "run_id"), ("out_dict", "metadata")]), (addprov, datasink, [("out_prov", "provenance")]), (outliers, datasink, [(("out_file", _parse_tout), "aor")]), (gcor, datasink, [(("out", _tofloat), "gcor")]), (quality, datasink, [(("out_file", _parse_tqual), "aqi")]), (measures, datasink, [("out_qc", "root")]), (datasink, outputnode, [("out_file", "out_file")]), (meta, outputnode, [("out_dict", "meta_sidecar")]), ]) # fmt: on # FFT spikes finder if config.workflow.fft_spikes_detector: from .utils import slice_wise_fft spikes_fft = pe.Node( niu.Function( input_names=["in_file"], output_names=["n_spikes", "out_spikes", "out_fft"], function=slice_wise_fft, ), name="SpikesFinderFFT", ) # fmt: off workflow.connect([ (inputnode, spikes_fft, [("in_ras", "in_file")]), (spikes_fft, outputnode, [("out_spikes", "out_spikes"), ("out_fft", "out_fft")]), (spikes_fft, datasink, [("n_spikes", "spikes_num")]) ]) # fmt: on return workflow
[docs]def individual_reports(name="ReportsWorkflow"): """ Write out individual reportlets. .. workflow:: from mriqc.workflows.functional import individual_reports from mriqc.testing import mock_config with mock_config(): wf = individual_reports() """ from niworkflows.interfaces.plotting import FMRISummary from niworkflows.interfaces.morphology import BinaryDilation, BinarySubtraction from nireports.interfaces import PlotMosaic, PlotSpikes from mriqc.interfaces.functional import Spikes from mriqc.interfaces.reports import IndividualReport verbose = config.execution.verbose_reports mem_gb = config.workflow.biggest_file_gb pages = 5 extra_pages = int(verbose) * 4 workflow = pe.Workflow(name=name) inputnode = pe.Node( niu.IdentityInterface( fields=[ "in_iqms", "in_ras", "hmc_epi", "epi_mean", "brainmask", "hmc_fd", "fd_thres", "epi_parc", "in_dvars", "in_stddev", "outliers", "in_spikes", "in_fft", "mni_report", "ica_report", "meta_sidecar", ] ), name="inputnode", ) # Set FD threshold inputnode.inputs.fd_thres = config.workflow.fd_thres spmask = pe.Node( niu.Function( input_names=["in_file", "in_mask"], output_names=["out_file", "out_plot"], function=spikes_mask, ), name="SpikesMask", mem_gb=mem_gb * 3.5, ) spikes_bg = pe.Node( Spikes(no_zscore=True, detrend=False), name="SpikesFinderBgMask", mem_gb=mem_gb * 2.5, ) # Generate crown mask # Create the crown mask dilated_mask = pe.Node(BinaryDilation(), name="dilated_mask") subtract_mask = pe.Node(BinarySubtraction(), name="subtract_mask") parcels = pe.Node(niu.Function(function=_carpet_parcellation), name="parcels") bigplot = pe.Node(FMRISummary(), name="BigPlot", mem_gb=mem_gb * 3.5) # fmt: off workflow.connect([ (inputnode, spikes_bg, [("in_ras", "in_file")]), (inputnode, spmask, [("in_ras", "in_file")]), (inputnode, bigplot, [("hmc_epi", "in_func"), ("hmc_fd", "fd"), ("fd_thres", "fd_thres"), ("in_dvars", "dvars"), ("outliers", "outliers"), (("meta_sidecar", _get_tr), "tr")]), (inputnode, parcels, [("epi_parc", "segmentation")]), (inputnode, dilated_mask, [("brainmask", "in_mask")]), (inputnode, subtract_mask, [("brainmask", "in_subtract")]), (dilated_mask, subtract_mask, [("out_mask", "in_base")]), (subtract_mask, parcels, [("out_mask", "crown_mask")]), (parcels, bigplot, [("out", "in_segm")]), (spikes_bg, bigplot, [("out_tsz", "in_spikes_bg")]), (spmask, spikes_bg, [("out_file", "in_mask")]), ]) # fmt: on mosaic_mean = pe.Node( PlotMosaic( out_file="plot_func_mean_mosaic1.svg", cmap="Greys_r", ), name="PlotMosaicMean", ) mosaic_stddev = pe.Node( PlotMosaic( out_file="plot_func_stddev_mosaic2_stddev.svg", cmap="viridis", ), name="PlotMosaicSD", ) mplots = pe.Node( niu.Merge( pages + extra_pages + int(config.workflow.fft_spikes_detector) + int(config.workflow.ica) ), name="MergePlots", ) rnode = pe.Node(IndividualReport(), name="GenerateReport") # Link images that should be reported dsplots = pe.Node( nio.DataSink( base_directory=str(config.execution.output_dir), parameterization=False, ), name="dsplots", run_without_submitting=True, ) # fmt: off workflow.connect([ (inputnode, rnode, [("in_iqms", "in_iqms")]), (inputnode, mosaic_mean, [("epi_mean", "in_file")]), (inputnode, mosaic_stddev, [("in_stddev", "in_file")]), (mosaic_mean, mplots, [("out_file", "in1")]), (mosaic_stddev, mplots, [("out_file", "in2")]), (bigplot, mplots, [("out_file", "in3")]), (mplots, rnode, [("out", "in_plots")]), (rnode, dsplots, [("out_file", "@html_report")]), ]) # fmt: on if config.workflow.fft_spikes_detector: mosaic_spikes = pe.Node( PlotSpikes( out_file="plot_spikes.svg", cmap="viridis", title="High-Frequency spikes", ), name="PlotSpikes", ) # fmt: off workflow.connect([ (inputnode, mosaic_spikes, [("in_ras", "in_file"), ("in_spikes", "in_spikes"), ("in_fft", "in_fft")]), (mosaic_spikes, mplots, [("out_file", "in4")]) ]) # fmt: on if config.workflow.ica: page_number = 4 + config.workflow.fft_spikes_detector # fmt: off workflow.connect([ (inputnode, mplots, [("ica_report", "in%d" % page_number)]) ]) # fmt: on if not verbose: return workflow mosaic_zoom = pe.Node( PlotMosaic( out_file="plot_anat_mosaic1_zoomed.svg", cmap="Greys_r", ), name="PlotMosaicZoomed", ) mosaic_noise = pe.Node( PlotMosaic( out_file="plot_anat_mosaic2_noise.svg", only_noise=True, cmap="viridis_r", ), name="PlotMosaicNoise", ) if config.workflow.species.lower() in ("rat", "mouse"): mosaic_mean.inputs.view = ("coronal", "axial") mosaic_stddev.inputs.view = ("coronal", "axial") mosaic_zoom.inputs.view = ("coronal", "axial") mosaic_noise.inputs.view = ("coronal", "axial") # Verbose-reporting goes here from nireports.interfaces import PlotContours plot_bmask = pe.Node( PlotContours( display_mode="y" if config.workflow.species.lower() in ("rat", "mouse") else "z", levels=[0.5], colors=["r"], cut_coords=10, out_file="bmask", ), name="PlotBrainmask", ) # fmt: off workflow.connect([ (inputnode, plot_bmask, [("epi_mean", "in_file"), ("brainmask", "in_contours")]), (inputnode, mosaic_zoom, [("epi_mean", "in_file"), ("brainmask", "bbox_mask_file")]), (inputnode, mosaic_noise, [("epi_mean", "in_file")]), (mosaic_zoom, mplots, [("out_file", "in%d" % (pages + 1))]), (mosaic_noise, mplots, [("out_file", "in%d" % (pages + 2))]), (plot_bmask, mplots, [("out_file", "in%d" % (pages + 3))]), (inputnode, mplots, [("mni_report", "in%d" % (pages + 4))]), ]) # fmt: on return workflow
[docs]def fmri_bmsk_workflow(name="fMRIBrainMask"): """ Compute a brain mask for the input :abbr:`fMRI (functional MRI)` dataset. .. workflow:: from mriqc.workflows.functional import fmri_bmsk_workflow from mriqc.testing import mock_config with mock_config(): wf = fmri_bmsk_workflow() """ from nipype.interfaces.afni import Automask workflow = pe.Workflow(name=name) inputnode = pe.Node(niu.IdentityInterface(fields=["in_file"]), name="inputnode") outputnode = pe.Node(niu.IdentityInterface(fields=["out_file"]), name="outputnode") afni_msk = pe.Node(Automask(outputtype="NIFTI_GZ"), name="afni_msk") # Connect brain mask extraction # fmt: off workflow.connect([ (inputnode, afni_msk, [("in_file", "in_file")]), (afni_msk, outputnode, [("out_file", "out_file")]) ]) # fmt: on return workflow
[docs]def hmc(name="fMRI_HMC"): """ Create a :abbr:`HMC (head motion correction)` workflow for fMRI. .. workflow:: from mriqc.workflows.functional import hmc from mriqc.testing import mock_config with mock_config(): wf = hmc() """ from nipype.algorithms.confounds import FramewiseDisplacement from nipype.interfaces.afni import Despike, Refit, Volreg mem_gb = config.workflow.biggest_file_gb workflow = pe.Workflow(name=name) inputnode = pe.Node( niu.IdentityInterface(fields=["in_file", "fd_radius"]), name="inputnode", ) outputnode = pe.Node( niu.IdentityInterface(fields=["out_file", "out_fd"]), name="outputnode" ) # calculate hmc parameters hmc = pe.Node( Volreg(args="-Fourier -twopass", zpad=4, outputtype="NIFTI_GZ"), name="motion_correct", mem_gb=mem_gb * 2.5, ) # Compute the frame-wise displacement fdnode = pe.Node( FramewiseDisplacement(normalize=False, parameter_source="AFNI"), name="ComputeFD", ) # fmt: off workflow.connect([ (inputnode, fdnode, [("fd_radius", "radius")]), (hmc, outputnode, [("out_file", "out_file")]), (hmc, fdnode, [("oned_file", "in_file")]), (fdnode, outputnode, [("out_file", "out_fd")]), ]) # fmt: on # despiking, and deoblique deoblique_node = pe.Node(Refit(deoblique=True), name="deoblique") despike_node = pe.Node(Despike(outputtype="NIFTI_GZ"), name="despike") if config.workflow.despike and config.workflow.deoblique: # fmt: off workflow.connect([ (inputnode, despike_node, [("in_file", "in_file")]), (despike_node, deoblique_node, [("out_file", "in_file")]), (deoblique_node, hmc, [("out_file", "in_file")]), ]) # fmt: on elif config.workflow.despike: # fmt: off workflow.connect([ (inputnode, despike_node, [("in_file", "in_file")]), (despike_node, hmc, [("out_file", "in_file")]), ]) # fmt: on elif config.workflow.deoblique: # fmt: off workflow.connect([ (inputnode, deoblique_node, [("in_file", "in_file")]), (deoblique_node, hmc, [("out_file", "in_file")]), ]) # fmt: on else: # fmt: off workflow.connect([ (inputnode, hmc, [("in_file", "in_file")]), ]) # fmt: on return workflow
[docs]def epi_mni_align(name="SpatialNormalization"): """ Estimate the transform that maps the EPI space into MNI152NLin2009cAsym. The input epi_mean is the averaged and brain-masked EPI timeseries Returns the EPI mean resampled in MNI space (for checking out registration) and the associated "lobe" parcellation in EPI space. .. workflow:: from mriqc.workflows.functional import epi_mni_align from mriqc.testing import mock_config with mock_config(): wf = epi_mni_align() """ from nipype.interfaces.ants import ApplyTransforms, N4BiasFieldCorrection from niworkflows.interfaces.reportlets.registration import ( SpatialNormalizationRPT as RobustMNINormalization, ) from templateflow.api import get as get_template # Get settings testing = config.execution.debug n_procs = config.nipype.nprocs ants_nthreads = config.nipype.omp_nthreads workflow = pe.Workflow(name=name) inputnode = pe.Node( niu.IdentityInterface(fields=["epi_mean", "epi_mask"]), name="inputnode", ) outputnode = pe.Node( niu.IdentityInterface(fields=["epi_mni", "epi_parc", "report"]), name="outputnode", ) n4itk = pe.Node( N4BiasFieldCorrection(dimension=3, copy_header=True), name="SharpenEPI" ) norm = pe.Node( RobustMNINormalization( explicit_masking=False, flavor="testing" if testing else "precise", float=config.execution.ants_float, generate_report=True, moving="boldref", num_threads=ants_nthreads, reference="boldref", template=config.workflow.template_id, ), name="EPI2MNI", num_threads=n_procs, mem_gb=3, ) if config.workflow.species.lower() == "human": norm.inputs.reference_image = str( get_template(config.workflow.template_id, resolution=2, suffix="boldref") ) norm.inputs.reference_mask = str( get_template( config.workflow.template_id, resolution=2, desc="brain", suffix="mask", ) ) # adapt some population-specific settings else: from nirodents.workflows.brainextraction import _bspline_grid n4itk.inputs.shrink_factor = 1 n4itk.inputs.n_iterations = [50] * 4 norm.inputs.reference_image = str( get_template(config.workflow.template_id, suffix="T2w") ) norm.inputs.reference_mask = str( get_template( config.workflow.template_id, desc="brain", suffix="mask", )[0] ) bspline_grid = pe.Node( niu.Function(function=_bspline_grid), name="bspline_grid" ) # fmt: off workflow.connect([ (inputnode, bspline_grid, [('epi_mean', 'in_file')]), (bspline_grid, n4itk, [('out', 'args')]) ]) # fmt: on # Warp segmentation into EPI space invt = pe.Node( ApplyTransforms( float=True, dimension=3, default_value=0, interpolation="MultiLabel", ), name="ResampleSegmentation", ) if config.workflow.species.lower() == "human": invt.inputs.input_image = str( get_template( config.workflow.template_id, resolution=1, desc="carpet", suffix="dseg", ) ) else: invt.inputs.input_image = str( get_template( config.workflow.template_id, suffix="dseg", )[-1] ) # fmt: off workflow.connect([ (inputnode, invt, [("epi_mean", "reference_image")]), (inputnode, n4itk, [("epi_mean", "input_image")]), (n4itk, norm, [("output_image", "moving_image")]), (norm, invt, [ ("inverse_composite_transform", "transforms")]), (invt, outputnode, [("output_image", "epi_parc")]), (norm, outputnode, [("warped_image", "epi_mni"), ("out_report", "report")]), ]) # fmt: on if config.workflow.species.lower() == "human": workflow.connect([(inputnode, norm, [("epi_mask", "moving_mask")])]) return workflow
[docs]def spikes_mask(in_file, in_mask=None, out_file=None): """Calculate a mask in which check for :abbr:`EM (electromagnetic)` spikes.""" import os.path as op import nibabel as nb import numpy as np from nilearn.image import mean_img from nilearn.plotting import plot_roi from scipy import ndimage as nd if out_file is None: fname, ext = op.splitext(op.basename(in_file)) if ext == ".gz": fname, ext2 = op.splitext(fname) ext = ext2 + ext out_file = op.abspath("{}_spmask{}".format(fname, ext)) out_plot = op.abspath("{}_spmask.pdf".format(fname)) in_4d_nii = nb.load(in_file) orientation = nb.aff2axcodes(in_4d_nii.affine) if in_mask: mask_data = nb.load(in_mask).get_data() a = np.where(mask_data != 0) bbox = ( np.max(a[0]) - np.min(a[0]), np.max(a[1]) - np.min(a[1]), np.max(a[2]) - np.min(a[2]), ) longest_axis = np.argmax(bbox) # Input here is a binarized and intersected mask data from previous section dil_mask = nd.binary_dilation( mask_data, iterations=int(mask_data.shape[longest_axis] / 9) ) rep = list(mask_data.shape) rep[longest_axis] = -1 new_mask_2d = dil_mask.max(axis=longest_axis).reshape(rep) rep = [1, 1, 1] rep[longest_axis] = mask_data.shape[longest_axis] new_mask_3d = np.logical_not(np.tile(new_mask_2d, rep)) else: new_mask_3d = np.zeros(in_4d_nii.shape[:3]) == 1 if orientation[0] in ["L", "R"]: new_mask_3d[0:2, :, :] = True new_mask_3d[-3:-1, :, :] = True else: new_mask_3d[:, 0:2, :] = True new_mask_3d[:, -3:-1, :] = True mask_nii = nb.Nifti1Image( new_mask_3d.astype(np.uint8), in_4d_nii.affine, in_4d_nii.header ) mask_nii.to_filename(out_file) plot_roi(mask_nii, mean_img(in_4d_nii), output_file=out_plot) return out_file, out_plot
def _mean(inlist): import numpy as np return np.mean(inlist) def _parse_tqual(in_file): import numpy as np with open(in_file, "r") as fin: lines = fin.readlines() return np.mean([float(line.strip()) for line in lines if not line.startswith("++")]) def _parse_tout(in_file): import numpy as np data = np.loadtxt(in_file) # pylint: disable=no-member return data.mean() def _carpet_parcellation(segmentation, crown_mask): """Generate the union of two masks.""" from pathlib import Path import numpy as np import nibabel as nb img = nb.load(segmentation) lut = np.zeros((256,), dtype="uint8") lut[100:201] = 1 # Ctx GM lut[30:99] = 2 # dGM lut[1:11] = 3 # WM+CSF lut[255] = 4 # Cerebellum # Apply lookup table seg = lut[np.asanyarray(img.dataobj, dtype="uint16")] seg[np.asanyarray(nb.load(crown_mask).dataobj, dtype=int) > 0] = 5 outimg = img.__class__(seg.astype("uint8"), img.affine, img.header) outimg.set_data_dtype("uint8") out_file = Path("segments.nii.gz").absolute() outimg.to_filename(out_file) return str(out_file) def _get_tr(meta_dict): return meta_dict.get("RepetitionTime", None)