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Merge pull request #1886 from oesteban/rf/new-sdcflows-api
ENH: Upgrade SDCFlows to new API (1.0.0rc1)
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‎docs/sdc.rst

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Correction for geometric distortion in echo planar images from B0
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field variations Magn. Reson. Med., 34 (1) (1995), pp. 65-73,
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doi:`10.1002/mrm.1910340111 <https://doi.org/10.1002/mrm.1910340111>`_.
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.. [Hutton2002] Hutton et al., Image Distortion Correction in fMRI: A Quantitative
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Evaluation, NeuroImage 16(1):217-240, 2002. doi:`10.1006/nimg.2001.1054
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<https://doi.org/10.1006/nimg.2001.1054>`_.
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.. [Huntenburg2014] Huntenburg, J. M. (2014) Evaluating Nonlinear
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Coregistration of BOLD EPI and T1w Images. Berlin: Master
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Thesis, Freie Universität. `PDF
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<http://pubman.mpdl.mpg.de/pubman/item/escidoc:2327525:5/component/escidoc:2327523/master_thesis_huntenburg_4686947.pdf>`_.
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.. [Treiber2016] Treiber, J. M. et al. (2016) Characterization and Correction
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of Geometric Distortions in 814 Diffusion Weighted Images,
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PLoS ONE 11(3): e0152472. doi:`10.1371/journal.pone.0152472
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<https://doi.org/10.1371/journal.pone.0152472>`_.
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.. [Wang2017] Wang S, et al. (2017) Evaluation of Field Map and Nonlinear
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Registration Methods for Correction of Susceptibility Artifacts
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in Diffusion MRI. Front. Neuroinform. 11:17.
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doi:`10.3389/fninf.2017.00017
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<https://doi.org/10.3389/fninf.2017.00017>`_.
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‎fmriprep/cli/run.py

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# Generate reports phase
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failed_reports = generate_reports(
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subject_list, output_dir, work_dir, run_uuid, packagename='fmriprep')
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subject_list, output_dir, work_dir, run_uuid,
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config=pkgrf('fmriprep', 'data/reports-spec.yml'),
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packagename='fmriprep')
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write_derivative_description(bids_dir, output_dir / 'fmriprep')
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if failed_reports and not opts.notrack:
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# Called with reports only
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if opts.reports_only:
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from pkg_resources import resource_filename as pkgrf
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build_log.log(25, 'Running --reports-only on participants %s', ', '.join(subject_list))
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if opts.run_uuid is not None:
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run_uuid = opts.run_uuid
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retval['run_uuid'] = run_uuid
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retval['return_code'] = generate_reports(
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subject_list, output_dir, work_dir, run_uuid,
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config=pkgrf('fmriprep', 'data/reports-spec.yml'),
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packagename='fmriprep')
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return retval
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‎fmriprep/data/reports-spec.yml

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package: niworkflows
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sections:
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- name: Summary
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reportlets:
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- bids: {datatype: anat, desc: summary, suffix: T1w}
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- name: Anatomical
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reportlets:
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- bids:
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datatype: anat
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desc: conform
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extension: [.html]
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suffix: T1w
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- bids: {datatype: anat, suffix: dseg}
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caption: This panel shows the template T1-weighted image (if several T1w images
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were found), with contours delineating the detected brain mask and brain tissue
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segmentations.
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subtitle: Brain mask and brain tissue segmentation of the T1w
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- bids: {datatype: anat, space: .*, suffix: T1w, regex_search: True}
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caption: Spatial normalization of the T1w image to the <code>{space}</code> template.
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description: Results of nonlinear alignment of the T1w reference one or more template
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space(s). Hover on the panels with the mouse pointer to transition between both
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spaces.
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static: false
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subtitle: Spatial normalization of the anatomical T1w reference
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- bids: {datatype: anat, desc: reconall, suffix: T1w}
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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: Functional
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ordering: session,task,run
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reportlets:
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- bids: {datatype: func, desc: summary, suffix: bold}
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- bids: {datatype: func, desc: validation, suffix: bold}
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- bids: {datatype: func, desc: fieldmap, suffix: bold}
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caption: The estimated fieldmap was aligned to the corresponding EPI reference
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with a rigid-registration process of the magintude part of the fieldmap,
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using <code>antsRegistration</code>.
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Overlaid on top of the co-registration results, the displacements along the
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phase-encoding direction are represented in arbitrary units.
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Please note that the color scale is centered around zero (i.e. full transparency),
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but the extremes might be different (i.e., the maximum of red colors could be
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orders of magnitude above or below the minimum of blue colors.)
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static: false
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subtitle: Estimated fieldmap and alignment to the corresponding EPI reference
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- bids: {datatype: func, desc: sdc, suffix: bold}
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caption: Results of performing susceptibility distortion correction (SDC) on the
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EPI
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static: false
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subtitle: Susceptibility distortion correction
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- bids: {datatype: func, desc: forcedsyn, suffix: bold}
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caption: The dataset contained some fieldmap information, but the argument <code>--force-syn</code>
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was used. The higher-priority SDC method was used. Here, we show the results
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of performing SyN-based SDC on the EPI for comparison.
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static: false
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subtitle: Experimental fieldmap-less susceptibility distortion correction
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- bids: {datatype: func, desc: flirtnobbr, suffix: bold}
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caption: FSL <code>flirt</code> was used to generate transformations from EPI
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space to T1 Space - BBR refinement rejected. Note that Nearest Neighbor interpolation
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is used in the reportlets in order to highlight potential spin-history and other
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artifacts, whereas final images are resampled using Lanczos interpolation.
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static: false
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subtitle: Alignment of functional and anatomical MRI data (volume based)
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- bids: {datatype: func, desc: coreg, suffix: bold}
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caption: <code>mri_coreg</code> (FreeSurfer) was used to generate transformations
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from EPI space to T1 Space - <code>bbregister</code> refinement rejected. Note
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that Nearest Neighbor interpolation is used in the reportlets in order to highlight
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potential spin-history and other artifacts, whereas final images are resampled
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using Lanczos interpolation.
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static: false
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subtitle: Alignment of functional and anatomical MRI data (volume based)
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- bids: {datatype: func, desc: flirtbbr, suffix: bold}
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caption: FSL <code>flirt</code> was used to generate transformations from EPI-space
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to T1w-space - The white matter mask calculated with FSL <code>fast</code> (brain
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tissue segmentation) was used for BBR. Note that Nearest Neighbor interpolation
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is used in the reportlets in order to highlight potential spin-history and other
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artifacts, whereas final images are resampled using Lanczos interpolation.
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static: false
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subtitle: Alignment of functional and anatomical MRI data (surface driven)
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- bids: {datatype: func, desc: bbregister, suffix: bold}
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caption: <code>bbregister</code> was used to generate transformations from EPI-space
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to T1w-space. Note that Nearest Neighbor interpolation is used in the reportlets
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in order to highlight potential spin-history and other artifacts, whereas final
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images are resampled using Lanczos interpolation.
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static: false
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subtitle: Alignment of functional and anatomical MRI data (surface driven)
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- bids: {datatype: func, desc: rois, suffix: bold}
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caption: Brain mask calculated on the BOLD signal (red contour), along with the
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masks used for a/tCompCor.<br />The aCompCor mask (magenta contour) is a conservative
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CSF and white-matter mask for extracting physiological and movement confounds.
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<br />The fCompCor mask (blue contour) contains the top 5% most variable voxels
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within a heavily-eroded brain-mask.
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subtitle: Brain mask and (temporal/anatomical) CompCor ROIs
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- bids:
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datatype: func
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desc: '[at]compcor'
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extension: [.html]
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suffix: bold
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- bids: {datatype: func, desc: 'compcorvar', suffix: bold}
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caption: The cumulative variance explained by the first k components of the
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<em>t/aCompCor</em> decomposition, plotted for all values of <em>k</em>.
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The number of components that must be included in the model in order to
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explain some fraction of variance in the decomposition mask can be used
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as a feature selection criterion for confound regression.
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subtitle: Variance explained by t/aCompCor components
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- bids: {datatype: func, desc: carpetplot, suffix: bold}
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caption: Summary statistics are plotted, which may reveal trends or artifacts
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in the BOLD data. Global signals calculated within the whole-brain (GS), within
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the white-matter (WM) and within cerebro-spinal fluid (CSF) show the mean BOLD
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signal in their corresponding masks. DVARS and FD show the standardized DVARS
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and framewise-displacement measures for each time point.<br />A carpet plot
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shows the time series for all voxels within the brain mask. Voxels are grouped
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into cortical (blue), and subcortical (orange) gray matter, cerebellum (green)
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and white matter and CSF (red), indicated by the color map on the left-hand
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side.
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subtitle: BOLD Summary
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- bids: {datatype: func, desc: 'confoundcorr', suffix: bold}
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caption: |
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Left: Heatmap summarizing the correlation structure among confound variables.
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(Cosine bases and PCA-derived CompCor components are inherently orthogonal.)
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Right: magnitude of the correlation between each confound time series and the
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mean global signal. Strong correlations might be indicative of partial volume
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effects and can inform decisions about feature orthogonalization prior to
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confound regression.
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subtitle: Correlations among nuisance regressors
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- bids: {datatype: func, desc: aroma, suffix: bold}
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caption: |
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Maps created with maximum intensity projection (glass brain) with a
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black brain outline. Right hand side of each map: time series (top in seconds),
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frequency spectrum (bottom in Hertz). Components classified as signal are plotted
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in green; noise components in red.
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subtitle: ICA Components classified by AROMA
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- name: About
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reportlets:
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- bids: {datatype: anat, desc: about, suffix: T1w}

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