aslprep.workflows.asl.resampling module

Workflows for resampling data.

init_asl_preproc_trans_wf(mem_gb, omp_nthreads, name='asl_preproc_trans_wf', use_compression=True, use_fieldwarp=False, split_file=False, interpolation='LanczosWindowedSinc')[source]

Resample in native (original) space.

This workflow resamples the input fMRI in its native (original) space in a “single shot” from the original asl series.

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

(Source code, png, svg, pdf)

Parameters:

  • 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_std_trans_wf)

  • use_compression (bool) – Save registered asl series as .nii.gz

  • use_fieldwarp (bool) – Include SDC warp in single-shot transform from asl to MNI

  • split_file (bool) – Whether the input file should be splitted (it is a 4D file) or it is a list of 3D files (default False, do not split)

  • interpolation (str) – Interpolation type to be used by ANTs’ applyTransforms (default 'LanczosWindowedSinc')

Inputs:

  • asl_file – Individual 3D volumes, not motion corrected

  • asl_mask – Skull-stripping mask of reference image

  • name_source – asl series NIfTI file Used to recover original information lost during processing

  • hmc_xforms – List of affine transforms aligning each volume to ref_image in ITK format

  • fieldwarp – a DFM in ITK format

Outputs:

  • asl – asl series, resampled in native space, including all preprocessing

  • asl_mask – asl series mask calculated with the new time-series

  • asl_ref – asl reference image: an average-like 3D image of the time-series

  • asl_ref_brain – Same as asl_ref, but once the brain mask has been applied

init_asl_std_trans_wf(mem_gb, omp_nthreads, spaces, scorescrub=False, basil=False, name='asl_std_trans_wf', use_compression=True, use_fieldwarp=False)[source]

Sample ASL into standard space with a single-step resampling of the original ASL series.

Important

This workflow provides two outputnodes. One output node (with name poutputnode) will be parameterized in a Nipype sense (see Nipype iterables), and a second node (outputnode) will collapse the parameterized outputs into synchronous lists of the output fields listed below.

Workflow Graph
../_images/aslprep-workflows-asl-resampling-2.png

(Source code, png, svg, pdf)

Parameters:

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

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

  • spaces (SpatialReferences) – A container for storing, organizing, and parsing spatial normalizations. Composed of Reference objects representing spatial references. Each Reference contains a space, which is a string of either TemplateFlow template IDs.

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

  • use_compression (bool) – Save registered ASL series as .nii.gz

  • use_fieldwarp (bool) – Include SDC warp in single-shot transform from ASL to MNI

Inputs:

  • anat2std_xfm – List of anatomical-to-standard space transforms generated during spatial normalization.

  • asl_mask – Skull-stripping mask of reference image

  • asl_split – Individual 3D volumes, not motion corrected

  • fieldwarp – a DFM in ITK format

  • hmc_xforms – List of affine transforms aligning each volume to ref_image in ITK format

  • itk_asl_to_t1 – Affine transform from ref_asl_brain to T1 space (ITK format)

  • name_source – ASL series NIfTI file Used to recover original information lost during processing

  • templates – List of templates that were applied as targets during spatial normalization.

Outputs:

  • asl_std – ASL series, resampled to template space

  • cbf_std, *cbf – cbf series, resampled to template space

  • asl_std_ref – Reference, contrast-enhanced summary of the ASL series, resampled to template space

  • asl_mask_std – ASL series mask in template space

  • template – Template identifiers synchronized correspondingly to previously described outputs.

init_asl_surf_wf(mem_gb, surface_spaces, medial_surface_nan, name='asl_surf_wf')[source]

Sample functional images to FreeSurfer surfaces.

For each vertex, the cortical ribbon is sampled at six points (spaced 20% of thickness apart) and averaged. Outputs are in GIFTI format.

Workflow Graph
../_images/aslprep-workflows-asl-resampling-3.png

(Source code, png, svg, pdf)

Parameters:

  • surface_spaces (list) – List of FreeSurfer surface-spaces (either fsaverage{3,4,5,6,} or fsnative) the functional images are to be resampled to. For fsnative, images will be resampled to the individual subject’s native surface.

  • medial_surface_nan (bool) – Replace medial wall values with NaNs on functional GIFTI files

Inputs:

  • source_file – Motion-corrected ASL series in T1 space

  • t1w_preproc – Bias-corrected structural template image

  • subjects_dir – FreeSurfer SUBJECTS_DIR

  • subject_id – FreeSurfer subject ID

  • t1w2fsnative_xfm – LTA-style affine matrix translating from T1w to FreeSurfer-conformed subject space

Outputs:

surfaces – ASL series, resampled to FreeSurfer surfaces