Flexible, ultrahigh-throughput and easy-implementing distributed coincidence processor for improving PET imaging performance

本项目旨在开发一种新型的创新巧合处理器，以克服传统巧合处理器的局限性和缺点

This project aims to develop a new and innovative coincidence processor to overcome the limit and drawback of centralized coincidence processor (CCP) that has been used by many commercial and research Positron Emission Tomography (PET) scanners since the initial development of PET. Whereas CCP handles the entire task of PET coincidence event selection with a single complex central processor, similarly to a computer central processing unit (CPU), that it has the limited count-rate of processing coincidence data because of a single processor and it is too complex to implement online on a field-programmable-gate-array (FPGA) for many research groups without extensive expertise and resources, we propose to use a network of distributed coincidence processors (DCP) that work independently and in parallel to process coincidence data for each detector pair with its dedicated coincidence processor, similar to a graphics processing unit (GPU). By breaking a single complex system-level coincidence process into many simple detector-pair-level processes, DCP can significantly reduce the processing delay at each CP level and therefore increase the overall data throughput, even with a large number of detector pairs. The algorithm for coincidence event selection with a single detector pair is simple and can be easily implemented, tested with one detector pair and be straightforwardly replicated (or populated) to the rest. The goal of this proposed project is to design, implement, evaluate, enhance, and disseminate the proposed DCP technology. We will pursue three specific aims to achieve this project goal: (1) To design DCP technology, including the hardware infrastructure of DCP components and functions and firmware program to realize the design DCP components and functions on FPGA. (2) To implement DCP on a single FPGA board with 400 coincidence processors as a practical solution to a PET with small to medium number detector pairs, and on two FPGA boards with 50 coincidence processors on each board as an example of an expandable solution to a PET with a large number detector pairs; to evaluate DCP with pulsed signals and PET detectors, and enhance the DCP capability and performance with an iterative design and development process. 3) To document and disseminate DCP technology through publications and a website with downloadable technical documentations and firmware/software code. If successfully developed, DCP will provide a novel and different technology platform for coincidence processing to solve the problems with CCP. As a game changer, DCP can yield a very high count-rate PET online coincidence data acquisition far beyond the limit of what CCP can provide and can be implemented on FPGA with much less technical challenging than implementing CCP. By addressing the problems with CCP and providing the solutions to the research community, this project would have a transformative impact on improving the capability and performance of PET imaging and accelerating the development of new PET systems and technologies.