Real-time Reconstruction for a Scanning CMOS Intraoperative Probe by Deep Learning

The difficulty in delineating the boundary between cancerous and healthy tissue during cancer resection surgeries often leads to suboptimal surgical outcomes due to either an incomplete removal of cancerous residuals or an excess removal of healthy tissue. The labelling of cancer cells with radiotracers which can be detected by intraoperative probes presents a potential solution for tumour localisation to facilitate excision. In this study, the feasibility of reconstructing the radiotracer distribution in real-time from sensor array outputs (SAOs) obtained with an intraoperative probe utilising CMOS monolithic active pixel sensors is explored through the use of a convolutional encoder-decoder network. The network takes SAOs containing all detected event clusters from radiotracer emissions obtained by scanning the probe over a region of interest as input and the outputs a reconstructed radiotracer distribution within the scanned region of interest. This initial work demonstrates that the network is able to reconstruct simulated 2D piece-wise constant radiotracer distributions from synthesised SAOs containing beta and gamma clusters isolated from experimentally obtained SAOs using the intraoperative probe.