FPGA Based Electronics for PET Detector Modules With Neural Network Position Estimators

Abstract

We are currently developing a prototype monolithic scintillator PET detector module based on neural network position estimators. The detector module comprises a 25.5 mm × 25.5 mm × 10 mm LYSO crystal coupled to a Hamamatsu 64 channels multi-anode PMT H7546B. The electronics for the detector module reads out all the signal channels, which represents the distribution of the scintillating light for each 7 event, and calculates the impinging position according to the pre-defined neural network algorithms if the event satisfies the energy and timing selection conditions. Compared with classical pixelated detectors, a monolithic scintillator based detector module features a simpler design, lower cost, and better energy resolution, but has lower signal to noise ratio and a more complicated signal readout scheme and data processing. By Monte-Carlo simulation, the performances of several readout schemes were compared. An optimized readout scheme which combines the 64 channels into 16 digitized signals was adopted in our electronics design. After the high resolution signal waveform digitization, an FPGA takes charge of the remaining digital signal processing, including the on-line hardware execution of the neural network positioning algorithms. We have implemented the electronics system for the detector modules. A pipelined implementation of the optimized neural network algorithms in the FPGA is able to process up to 15.3 M events per second without loss of performance compared to an off-line implementation. In addition to the function validation tests, the preliminary performance of the detector module we are building for a PET system is also reported.