Real Time Artificial Neural Network FPGA Implementation for Triple Coincidences Recovery in PET

Abstract

In small-animal Positron Emission Tomography (PET), spatial resolution improvements rely on detector minimization in size and often come at the expense of lowering the detector photoelectric fraction. As a result, Inter-Crystal Scatter (ICS) occurrences are increased and affect the overall PET detection efficiency. To reclaim some lost efficiency, previous work used an artificial neural network (ANN) to identify the true line of response (LOR) for the simplest multiple event detection case, three coincident singles known as triplets. Despite promising results, this method is limited to an offline processing which is impractical when a limited data bandwidth is present between the scanner and the PC. This paper demonstrates the capability of processing triplets in real time using an ANN implemented in the field-programmable gate array (FPGA). The ANN pipelined architecture can process over 1 million triplets/second using less than 6000 FPGA slices. Real time processing on the LabPET I scanner yielded an overall 39.7% increase in detection efficiency relative to traditional high resolution settings with a 360-660 keV energy window along with a slight Contrast-to-Noise Ratio ( $CNR$ ) degradation. Although improvements are still possible, the proposed FPGA implementation proves the usability of an ANN in real time PET applications in conditions where spare computational resources are limited and the data rate to be processed is high.