aslprep.workflows.asl.hmc module

Workflows for estimating and correcting head motion in ASL images.

init_asl_hmc_wf(mem_gb, omp_nthreads, name='asl_hmc_wf')[source]

Estimate head-motion parameters and optionally correct them for intensity differences.

This workflow separately estimates motion parameters for each unique type of volume (e.g., control, label, deltam, M0, CBF), and then stitches the resulting parameters back together according to the aslcontext file.

Workflow Graph
../_images/aslprep-workflows-asl-hmc-1.png

(Source code, png, svg, pdf)

Parameters:

  • processing_target ({“control”, “deltam”, “cbf”})

  • m0type ({“Separate”, “Included”, “Absent”, “Estimate”})

  • mem_gb (float) – Size of ASL file in GB

  • omp_nthreads (int) – Maximum number of threads an individual process may use

  • name (str) – Name of workflow (default: asl_hmc_wf)

Inputs:

  • asl_file – Control-label pair series NIfTI file. If an ASL run contains M0 volumes, deltaM volumes, or CBF volumes, those volumes should be removed before running this workflow.

  • aslcontext – ASL context TSV file.

  • raw_ref_image – Reference image to which ASL series is motion corrected

Outputs:

  • xforms – ITKTransform file aligning each volume to ref_image

  • movpar_file – MCFLIRT motion parameters, normalized to SPM format (X, Y, Z, Rx, Ry, Rz)

  • rms_file – Framewise displacement as measured by fsl_motion_outliers

Notes

ASLPrep uses volume type-wise motion correction [1] instead of the zig-zag regression approach [2] because it is unclear how M0 volumes should be treated in the zig-zag method.

References