GPU-Based Real-Time Software Coincidence Processing for Digital PET System

In some modern digital positron emission tomography (PET) systems, the coincidence pairs are extracted by software coincidence processing (SCP) for reconstruction. The SCP is typically implemented on central processing units (CPUs) and then it is accelerated by CPU multithreading technology. However, the more detection modules a PET system has, the more CPU threads are used in acquisition and the fewer threads are available in coincidence processing when the number of threads is fixed. This phenomenon results in reduced processing performance of CPU-based SCP, which limits the application of SCP. In this article, we propose low-cost GPU-based real-time SCP (GPU-SCP) methods to solve the limited CPU thread problem. The proposed processing architecture simplifies the management of threads between acquisition and coincidence processing, leading to the decouple of acquisition and coincidence, accelerates the coincidence processing by GPU multiple threads, and finally realizes the online coincidence processing in high-sensitivity digital PET systems with 20 basic detection modules (BDMs). To evaluate the performance of the proposed GPU-based SCP approaches, we adapted them to the home-made PET systems with different architectures. The speedup experimental results show that the proposed sorting-based GPU-SCP achieves up to similar to 15 times average speedup on GTX1070 compared with serial CPU algorithms, which is comparable to the parallel CPU algorithm with 30 threads. Besides, the proposed combination-based GPU-SCP is superior to the sorting-based GPU-SCP for a specific system architecture. Besides, the image quality experiments indicate the reconstruction images processed by GPU-SCPs are almost the same as the ground truth (differences similar to 1% in the image domain).