smriprep.workflows.fit.registration module
Spatial normalization workflows.
- smriprep.workflows.fit.registration.init_register_template_wf(*, sloppy, omp_nthreads, templates, name='register_template_wf')[source]
Build an individual spatial normalization workflow using
antsRegistration
.- Workflow Graph
-
(Source code, png, svg, pdf)
Important
This workflow defines an iterable input over the input parameter
templates
, so Nipype will produce one copy of the downstream workflows which connectpoutputnode.template
orpoutputnode.template_spec
to their inputs (poutputnode
stands for parametric output node). Nipype refers to this expansion of the graph as parameterized execution. If a joint list of values is required (and thus cutting off parameterization), please use the equivalent outputs ofoutputnode
(which joins all the parameterized execution paths).- Parameters:
sloppy (
bool
) – Apply sloppy arguments to speed up processing. Use with caution, registration processes will be very inaccurate.omp_nthreads (
int
) – Maximum number of threads an individual process may use.templates (
list
ofstr
) – List of standard space fullnames (e.g.,MNI152NLin6Asym
orMNIPediatricAsym:cohort-4
) which are targets for spatial normalization.
- Inputs:
moving_image – The input image that will be normalized to standard space.
lesion_mask – (optional) A mask to exclude regions from the cost-function input domain to enable standardization of lesioned brains.
template – Template name and specification
- Outputs:
anat2std_xfm – The T1w-to-template transform.
std2anat_xfm – The template-to-T1w transform.
template – Template name extracted from the input parameter
template
, for further use in downstream nodes.template_spec – Template specifications extracted from the input parameter
template
, for further use in downstream nodes.