Real-Time-Capable GPU-Framework for Depth-Aware Rigid 2D/3D Registration

Abstract

2D/3D image fusion is used for a variety of interventional procedures. Overlays of 2D images with perspective-correctly rendered 3D images provide the physicians additional information during the interventions. In this work, a real-time capable 2D/3D registration framework is presented. An adapted parallelization using GPU is investigated for the depth-aware registration algorithm. The GPU hardware architecture is specially taken into account by optimizing memory access patterns and exploiting CUDA-texture memory. The real-time capability is achieved with a median runtime of one 2D/3D registration iteration of 86.1ms with an median accuracy of up to 1.15 mm.