GDL-FIRE4D: Deep Learning-based fast 4D CT image registration

Deformable image registration (DIR) in thoracic 4D CT image data is integral for, e.g., radiotherapy treatment planning, but time consuming. Deep learning (DL)-based DIR promises speed-up, but present solutions are limited to small image sizes. In this paper, we propose a General Deep Learning-based Fast Image Registration framework suitable for application to clinical 4D CT data (GDL-FIRE4D). Open source DIR frameworks are selected to build GDL-FIRE4D variants. In-house acquired 4D CT images serve as training and open 4D CT data repositories as external evaluation cohorts. Taking up current attempts to DIR uncertainty estimation, dropout-based uncertainty maps for GDLFIRE4D variants are analyzed. We show that (1) registration accuracy of GDL-FIRE4D and standard DIR are in the same order; (2) computation
time is reduced to a few seconds (here: 60-fold speed-up); and (3) dropout-based uncertainty maps do not correlate to across-DIR vector field differences, raising doubts about applicability in the given context.