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

Purpose: Dual-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. Methods: It 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. Results: Simulation 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×. Conclusion: We have proposed a new acceleration algorithm for the dual-head PET system, in which both the forward and backprojection operations are accelerated by GPU.