sdcflows.workflows.syn module

Estimating the susceptibility distortions without fieldmaps.

Fieldmap-less estimation (experimental)

In the absence of direct measurements of fieldmap data, we provide an (experimental) option to estimate the susceptibility distortion based on the ANTs symmetric normalization (SyN) technique. This feature may be enabled, using the --use-syn-sdc flag, and will only be applied if fieldmaps are unavailable.

During the evaluation phase, the --force-syn flag will cause this estimation to be performed in addition to fieldmap-based estimation, to permit the direct comparison of the results of each technique. Note that, even if --force-syn is given, the functional outputs of FMRIPREP will be corrected using the fieldmap-based estimates.

Feedback will be enthusiastically received.

sdcflows.workflows.syn.init_syn_sdc_wf(omp_nthreads, epi_pe=None, atlas_threshold=3, name='syn_sdc_wf')[source]

Build the fieldmap-less susceptibility-distortion estimation workflow.

This workflow takes a skull-stripped T1w image and reference BOLD image and estimates a susceptibility distortion correction warp, using ANTs symmetric normalization (SyN) and the average fieldmap atlas described in [Treiber2016].

SyN deformation is restricted to the phase-encoding (PE) direction. If no PE direction is specified, anterior-posterior PE is assumed.

SyN deformation is also restricted to regions that are expected to have a >3mm (approximately 1 voxel) warp, based on the fieldmap atlas.

This technique is a variation on those developed in [Huntenburg2014] and [Wang2017].

Workflow Graph

(Source code, png, svg, pdf)

  • in_reference – reference image

  • in_reference_brain – skull-stripped reference image

  • t1w_brain – skull-stripped, bias-corrected structural image

  • std2anat_xfm – inverse registration transform of T1w image to MNI template

  • out_reference – the in_reference image after unwarping

  • out_reference_brain – the in_reference_brain image after unwarping

  • out_warp – the corresponding DFM compatible with ANTs

  • out_mask – mask of the unwarped input file



Treiber, J. M. et al. (2016) Characterization and Correction of Geometric Distortions in 814 Diffusion Weighted Images, PLoS ONE 11(3): e0152472. doi:10.1371/journal.pone.0152472.


Wang S, et al. (2017) Evaluation of Field Map and Nonlinear Registration Methods for Correction of Susceptibility Artifacts in Diffusion MRI. Front. Neuroinform. 11:17. doi:10.3389/fninf.2017.00017.


Huntenburg, J. M. (2014) Evaluating Nonlinear Coregistration of BOLD EPI and T1w Images. Berlin: Master Thesis, Freie Universität. PDF.