Workflows

Anatomical workflow

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:

(Source code, png, svg, pdf)

mriqc.workflows.anatomical.airmsk_wf(name='AirMaskWorkflow')

Implements the Step 1 of [Mortamet2009].

(Source code, png, svg, pdf)

mriqc.workflows.anatomical.anat_qc_workflow(name='anatMRIQC')

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

(Source code)

mriqc.workflows.anatomical.compute_iqms(name='ComputeIQMs')

Setup the workflow that actually computes the IQMs.

(Source code)

mriqc.workflows.anatomical.gradient_threshold(in_file, in_segm, thresh=1.0, out_file=None)

Compute a threshold from the histogram of the magnitude gradient image

mriqc.workflows.anatomical.headmsk_wf(name='HeadMaskWorkflow')

Computes a head mask as in [Mortamet2009].

(Source code, png, svg, pdf)

mriqc.workflows.anatomical.image_gradient(in_file, snr, out_file=None)

Computes the magnitude gradient of an image using numpy

mriqc.workflows.anatomical.individual_reports(name='ReportsWorkflow')

Generate the components of the individual report.

(Source code, png, svg, pdf)

mriqc.workflows.anatomical.spatial_normalization(name='SpatialNormalization', resolution=2)

Create a simplied workflow to perform fast spatial normalization.

Functional workflow

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')

Initialize the workflow that actually computes the IQMs.

(Source code)

mriqc.workflows.functional.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.

(Source code, png, svg, pdf)

mriqc.workflows.functional.fmri_bmsk_workflow(name='fMRIBrainMask')

Compute a brain mask for the input fMRI dataset.

(Source code, png, svg, pdf)

mriqc.workflows.functional.fmri_qc_workflow(name='funcMRIQC')

Initialize the (f)MRIQC workflow.

(Source code)

mriqc.workflows.functional.hmc(name='fMRI_HMC')

Create a HMC workflow for fMRI.

(Source code, png, svg, pdf)

mriqc.workflows.functional.individual_reports(name='ReportsWorkflow')

Write out individual reportlets.

(Source code, png, svg, pdf)

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

Calculate a mask in which check for EM spikes.

mriqc.workflows.anat_qc_workflow(name='anatMRIQC')

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

(Source code)

mriqc.workflows.fmri_qc_workflow(name='funcMRIQC')

Initialize the (f)MRIQC workflow.

(Source code)