GPU Accelerated Stochastic Origin Ensemble Method With List-Mode Data for Compton Camera Imaging in Proton Therapy

Proton beam therapy has the advantages over other forms of radiation therapy because it does less damage to healthy tissue while treating cancerous tissue. These advantages arise from the high dose deposition at the end of the proton beam which is known as the Bragg peak. This paper proposes a new architecture for graphics processing unit (GPU)-accelerated stochastic origin ensemble (SOE) algorithm, eventually achieves a great acceleration and allows to reconstruct images of the gamma radiation produced by proton beam in real time. The object of this paper is to improve the run time of SOE algorithm for improving the accuracy of the proton therapy. In previous studies, it is difficult to reconstruct images of prompt gamma (PG) ray in real time in proton therapy. After analyzing the structure of the SOE algorithm, we correspond each gamma ray detected by Compton camera (CC) to each thread in GPU, initialize the origin cones in GPU, and use data structures which are suitable for parallelization, shared memory, and random number lists to speed up parallel program. To evaluate the proposed method, we use the parallel SOE algorithm to reconstruct the sources with the CC imaging system, which is simulated by Geant4. The parallel program on the GPU eventually achieves a $25\times $ speedup over serial CPU run, reconstructing images of gamma radiation in 0.4 s. In the end, we also accelerate the Monte Carlo-based back projection (MC-BP) algorithm, the parallel program achieves a $79\times $ speedup over serial CPU run.