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

Background and PurposeValidation 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 MethodsSynthetic 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.ResultsGround-truth DVFs showed negligible ICE values, confirming mathematical invertibility. In HN phantoms, median ICE and GTRE were 0.8 ± 0.2 mm and 1.6 ± 0.4 mm, respectively. ICE correlated strongly with GTRE (R = 0.85, p < 0.001) and moderately with TRE (R = 0.68, p < 0.001). No significant correlation was found with contourbased MDA (2.47 ± 0.18 mm). Voxel-wise analysis showed that ICE captured spatial patterns of uncertainty consistent with regions of higher GTRE, while underestimating error for global homogeneous deformations >15 mm due to DIR regularisation. Across all datasets, ICE correctly identified high-uncertainty subregions that were not detected by contour-based metrics.ConclusionsICE enables automated voxel-wise quantification of DIR uncertainty directly from clinical DVFs. It complements traditional contour-based metrics and may support patient-specific QA and more reliable dose mapping in adaptive and re-irradiation radiotherapy workflows.