Comparison of GPU reconstruction based on different symmetries for dual-head PET.

Abstract

PurposeDual‐head positron emission tomography (PET) scanners have increasingly attracted the attention of many researchers. However, with the compact geometry, the depth‐of‐interaction blurring will reduce the image resolution considerably. Monte Carlo (MC)‐based system response matrix (SRM) is able to describe the physical process of PET imaging accurately and improve reconstruction quality significantly. The MC‐based SRM is large and precomputed, which leads to a longer image reconstruction time with indexing and retrieving precomputed system matrix elements. In this study, we proposed a GPU acceleration algorithm to accelerate the iterative reconstruction.MethodsIt has been demonstrated that the line‐of‐response (LOR)‐based symmetry and the Graphics Processing Unit (GPU) technology can accelerate the reconstruction tremendously. LOR‐based symmetry is suitable for the forward projection calculation, but not for the backprojection. In this study, we proposed a GPU acceleration algorithm that combined the LOR‐based symmetry and voxel‐based symmetry together, in which the LOR‐based symmetry is responsible for the forward projection, and the voxel‐based symmetry is used for the backprojection.ResultsSimulation and real experiments verify the efficiency of the algorithm. Compared with the CPU‐based calculation, the acceleration ratios of the forward projection and the backprojection operation are 130 and 110, respectively. The total acceleration ratio is 113×. In order to compare the acceleration effect of the different symmetries, we realized the reconstruction with the voxel‐based symmetry and the LOR‐based symmetry strategies. Compared with the LOR‐based GPU reconstruction, the acceleration ratio is 3.5×. Compared with the voxel‐based GPU reconstruction, the acceleration ratio is 12×.ConclusionWe have proposed a new acceleration algorithm for the dual‐head PET system, in which both the forward and backprojection operations are accelerated by GPU.