Correcting Charge Sharing Distortions in Photon Counting Detectors Utilising a Spatial-Temporal CNN.

Charge sharing induces spectral and spatial distortions on photon counting detectors which must be corrected using methods such as charge summing circuitry. We propose a method of correction using a spatial-temporal convolutional neural network based on the CycN-Net design. Our results were compared to an analytical scalar matrix correction and a U-Net. We show improvements in two energy channels set to 50 and 60 kev with a mean absolute percentage error reduced from 4.84% and 7.46% to 3.95% and 5.14% respectively when compared to the scalar matrix approach. We believe this shows the potential viability of utilising the spatial-temporal CNN approach for correcting charge sharing distortions in higher energy ranges, where photon counts tend to be lower for photon counting detectors.