Workflows

Anatomical workflow

_images/anatomical_workflow_source.svg

The anatomical workflow follows the following steps:

  1. Conform (reorientations, revise data types) input data and read associated metadata.

  2. Skull-stripping (AFNI).

  3. Calculate head mask – headmsk_wf().

  4. Spatial Normalization to MNI (ANTs)

  5. Calculate air mask above the nasial-cerebelum plane – airmsk_wf().

  6. Brain tissue segmentation (FAST).

  7. Extraction of IQMs – compute_iqms().

  8. Individual-reports generation – individual_reports().

This workflow is orchestrated by anat_qc_workflow().

For the skull-stripping, we use afni_wf from niworkflows.anat.skullstrip:

_images/workflows-1.png

(Source code, png, svg, pdf)

mriqc.workflows.anatomical.airmsk_wf(name='AirMaskWorkflow')[source]

Implements the Step 1 of [Mortamet2009].

_images/workflows-2.png

(Source code, png, svg, pdf)

mriqc.workflows.anatomical.anat_qc_workflow(name='anatMRIQC')[source]

One-subject-one-session-one-run pipeline to extract the NR-IQMs from anatomical images

_images/workflows-3.png

(Source code, png, svg, pdf)

mriqc.workflows.anatomical.compute_iqms(name='ComputeIQMs')[source]

Setup the workflow that actually computes the IQMs.

_images/workflows-4.png

(Source code, png, svg, pdf)

mriqc.workflows.anatomical.gradient_threshold(in_file, in_segm, thresh=15.0, out_file=None, aniso=False)[source]

Compute a threshold from the histogram of the magnitude gradient image

mriqc.workflows.anatomical.headmsk_wf(name='HeadMaskWorkflow')[source]

Computes a head mask as in [Mortamet2009].

_images/workflows-5.png

(Source code, png, svg, pdf)

mriqc.workflows.anatomical.image_gradient(in_file, snr, sigma=3.0, out_file=None)[source]

Computes the magnitude gradient of an image using numpy

mriqc.workflows.anatomical.individual_reports(name='ReportsWorkflow')[source]

Generate the components of the individual report.

_images/workflows-6.png

(Source code, png, svg, pdf)

mriqc.workflows.anatomical.sigma_calc(in_file)[source]
mriqc.workflows.anatomical.spatial_normalization(name='SpatialNormalization')[source]

Create a simplied workflow to perform fast spatial normalization.

mriqc.workflows.anatomical.synthstrip_wf(name='synthstrip_wf', omp_nthreads=None)[source]

Create a brain-extraction workflow using SynthStrip.

Functional workflow

_images/functional_workflow_source.svg

The functional workflow follows the following steps:

  1. Sanitize (revise data types and xforms) input data, read associated metadata and discard non-steady state frames.

  2. HMC based on 3dvolreg from AFNI – hmc().

  3. Skull-stripping of the time-series (AFNI) – fmri_bmsk_workflow().

  4. Calculate mean time-series, and tSNR.

  5. Spatial Normalization to MNI (ANTs) – epi_mni_align()

  6. Extraction of IQMs – compute_iqms().

  7. Individual-reports generation – individual_reports().

This workflow is orchestrated by fmri_qc_workflow().

mriqc.workflows.functional.compute_iqms(name='ComputeIQMs')[source]

Initialize the workflow that actually computes the IQMs.

_images/workflows-7.png

(Source code, png, svg, pdf)

mriqc.workflows.functional.epi_mni_align(name='SpatialNormalization')[source]

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.

_images/workflows-8.png

(Source code, png, svg, pdf)

mriqc.workflows.functional.fmri_bmsk_workflow(name='fMRIBrainMask')[source]

Compute a brain mask for the input fMRI dataset.

_images/workflows-9.png

(Source code, png, svg, pdf)

mriqc.workflows.functional.fmri_qc_workflow(name='funcMRIQC')[source]

Initialize the (f)MRIQC workflow.

_images/workflows-10.png

(Source code, png, svg, pdf)

mriqc.workflows.functional.hmc(name='fMRI_HMC')[source]

Create a HMC workflow for fMRI.

_images/workflows-11.png

(Source code, png, svg, pdf)

mriqc.workflows.functional.individual_reports(name='ReportsWorkflow')[source]

Write out individual reportlets.

_images/workflows-12.png

(Source code, png, svg, pdf)

mriqc.workflows.functional.spikes_mask(in_file, in_mask=None, out_file=None)[source]

Calculate a mask in which check for EM spikes.

mriqc.workflows.anat_qc_workflow(name='anatMRIQC')[source]

One-subject-one-session-one-run pipeline to extract the NR-IQMs from anatomical images

_images/workflows-13.png

(Source code, png, svg, pdf)

mriqc.workflows.fmri_qc_workflow(name='funcMRIQC')[source]

Initialize the (f)MRIQC workflow.

_images/workflows-14.png

(Source code, png, svg, pdf)