Inverse consistency error for validating deformable image registration: an explorative study on computational phantoms.

ABSTRACT:

Background and purpose

Validation of deformable image registration (DIR) remains predominantly contourbased; this study evaluated inverse consistency error (ICE) as an automated voxelwise metric for DIR accuracy.

Materials and methods

Synthetic ground-truth DVFs were generated using geometric and head-and-neck (HN) digital phantoms undergoing controlled global and local deformations. DIR was performed with the ANACONDA algorithm in RayStation. ICE maps derived from clinical DVFs were compared with ground-truth registration error (GTRE), target registration error (TRE) from 20 anatomical landmarks, and mean distance to agreement (MDA) for 22 propagated ROIs.

Results

Ground-truth DVFs showed negligible ICE values, confirming mathematical invertibility. In HN phantoms, m

SUBMITTER: Loi G 

PROVIDER: S-EPMC12925039 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature