Results from neural networks for recovery of PET triple coincidences

Abstract

High-resolution PET scanners with pixelated detectors have great sensitivity increase potential through the inclusion of multiple coincidences. Indeed, poor energy resolution and in-crystal detection mispositioning often prevent “traditional” Compton kinematics analysis from yielding high Line-of-Response (LOR) discrimination rates, while Bayesian methods are computationally expensive. Hence multiple coincidences are usually discarded when image degradation is not acceptable. This paper presents results from a new method to include Inter-Crystal Scatter (ICS) triple coincidences in the image without significant image degradation. The triple coincidences analyzed are the simplest inter-crystal Compton scatter scenario. Instead of mathematical models, the method employs geometry simplification of the raw energy and position measurements, which are then fed to a neural network. The paper quickly visits the algorithm structure, presents some Monte-Carlo validation results of the method with the LabPET model and shows images reconstructed from real data. The method achieves a 42% increase in sensitivity at the expense of a 10% degradation in contrast-to-noise ratio (CNR), with numerous potential improvements.