  Objective: Conventional event positioning algorithms in light-sharing PET
detectors are often limited by edge effects and the impact of inter-crystal
scattering (ICS). This study explores the feasibility of deep neural network
(DNN) techniques for more precise event positioning in finely segmented and
highly multiplexed PET detectors with light-sharing. Approach: A DNN was
designed for crystal localisation, and trained/tested with light distributions
of photoelectric (P) and Compton/photoelectric (CP) events simulated using
optical GATE and an efficient analytical method. Using the statistical
properties of ICS events from simulation, an energy-guided positioning
algorithm was built into the DNN, enabling selection of the unique or first
crystal of interaction in P and CP events, respectively. Performance of the DNN
was compared with Anger logic using light distributions from simulated 511-keV
point sources near the PET detector. Results: Despite coarse photodetector data
due to signal multiplexing, the DNN demonstrated a crystal classification
accuracy of 90% for P events and 82% for CP events. For crystal positioning,
the DNN outperformed Anger logic by at least 34% and 14% for P and CP events,
respectively. Further improvement is somewhat constrained by the physics,
specifically, the ratio of backward to forward scattering of gamma rays within
the crystal array being close to 1. This prevents selecting the first crystal
of interaction in CP events with a high degree of certainty. Significance:
Light-sharing and multiplexed PET detectors are common in high-resolution PET,
yet event positioning can be poor due to edge effects and ICS events. Our study
shows that DNN-based event positioning can enhance 2D coincidence event
positioning accuracy by nearly a factor of 2 compared to Anger logic. However,
further improvements are difficult to foresee without timing information.
