A GPU-based Architecture for Improved Online Rebinning Performance in Clinical 3-D PET

Online rebinning is an important and well-established technique for reducing the time required to process PET data. However, the need for efficient data processing in a clinical setting is growing rapidly and is beginning to exceed the capability of traditional online processing methods. High-count rate applications such as Rubidium 3-D PET studies can easily saturate current online rebinning technology. Real-time processing at these high count rates is essential to avoid significant data loss. In addition, the emergence of time-of-flight (TOF) scanners is producing very large data sets for processing. TOF applications require efficient online rebinning methods so as to maintain high patient throughput. Currently, several new hardware architectures like Graphics Processing Units (GPUs) are available to speed up the data parallel and number crunching algorithms. In comparison to the usual parallel systems, such as multiprocessor or clustered machines, CPU hardware can be much faster and above all, it is significantly cheaper. The GPUs have been primarily delivered for graphics for video games but are now being used for High Performance computing across many domains. In this study, we investigate the suitability of the GPU for PET Rebinning algorithms and also implement them on the same.