Memory-efficient volume ray tracing on GPU for radiotherapy

Abstract

Ray tracing within a uniform grid volume is a fundamental process invoked frequently by many radiation dose calculation methods in radiotherapy. Recent advances of the graphics processing units (GPU) help real-time dose calculation become a reachable goal. However, the performance of the known GPU methods for volume ray tracing is all bounded by the memory-throughput, which leads to inefficient usage of the GPU computational capacity. This paper introduces a simple yet effective ray tracing technique aiming to improve the memory bandwidth utilization of GPU for processing a massive number of rays. The idea is to exploit the coherent relationship between the rays and match the ray tracing behavior with the underlying characteristics of the GPU memory system. The proposed method has been evaluated on 4 phantom setups using randomly generated rays. The collapsed-cone convolution/superposition (CCCS) dose calculation method is also implemented with/without the proposed approach to verify the feasibility of our method. Compared with the direct GPU implementation of the popular 3DDDA algorithm, the new method provides a speedup in the range of 1.8–2.7X for the given phantom settings. Major performance factors such as ray origins, phantom sizes, and pyramid sizes are also analyzed. The proposed technique was also shown to lead to a speedup of 1.3–1.6X over the original GPU implementation of the CCCS algorithm.