GPU accelerated statistical image reconstruction for Compton cameras

Abstract

We propose GPU (graphics processing unit) accelerated methods that can dramatically improve the computational performance of statistical image reconstruction algorithms for Compton cameras. Since the conventional raybased backprojection method is inefficient for GPU, we develop a fully voxel-based backprojection method which can maximize the performance of GPU. In this method, the cone surface is sampled by the evenly distributed rays originated from the vertex of the cone. The intersecting chord length of the ray passing through a voxel is then approximated by the normal distance from the center of the voxel to the ray. Although this approximation can cause an error in backprojection, according to our simulation results, it does not noticeably affect the reconstruction. Our experimental phantom studies with the RAMLA (row-action maximum likelihood algorithm), which is a relaxed version of the OS-EM (ordered subsets expectation maximization) algorithm, indicate that the GPU-based method is roughly 50 times faster in computation time per iteration than the CPU-based method. According to our experimental results, for an acceptable 64×64×64 image reconstructed by RAMLA with 64 subsets and 8 iterations, the CPU-based method takes about 2.3 hours, whereas the GPU-based method takes only 2.7 minutes.