Self-organizing map neural network based positioning scheme for continuous crystal PET detectors

PET detectors based on continuous crystal could be an ideal alternative for current high resolution pixelated PET detectors if the issues of high performance position estimation and its real-time implementation are solved. Unfortunately, current existing position estimators are not much feasible for implementation on FPGA. In this paper, we propose a new self-organizing map (SOM) neural network based positioning scheme aiming at not only providing a high performance, but also being realistic for FPGA implementation. Benefitting from SOM feature mapping mechanism, the large set of input reference event data is approximated by a smaller set of prototype, which makes it easier to find the best pattern match for unknown events. Using our experimental data, the scheme is evaluated and compared with others. The test results show that SOM scheme has an equivalent positioning performance with previous k-NN method, and it is realistic to be implemented on FPGA achieving a high event processing throughput.