A GPGPU approach for accelerating 2-D/3-D rigid registration of medical images

Abstract

This paper presents a fast 2-D/3-D rigid registration method using a GPGPU approach, which stands for general-purpose computation on the graphics processing unit (GPU). Our method is based on an intensity-based registration algorithm using biplane images. To accelerate this algorithm, we execute three key procedures of 2-D/3-D registration on the GPU: digitally reconstructed radiograph (DRR) generation, gradient image generation, and normalized cross correlation (NCC) computation. We investigate the usability of our method in terms of registration time and robustness. The experimental results show that our GPU-based method successfully completes a registration task in about 10 seconds, demonstrating shorter registration time than a previous method based on a cluster computing approach.