Work Summary: Real-time imaging has potential to greatly increase the effectiveness of proton beam therapy for cancer treatment. One promising method of real-time imaging is the use of a Compton camera to detect prompt gamma rays, which are emitted along the path of the beam, in order to reconstruct their origin. However, because of limitations in the Compton camera’s ability to detect prompt gammas, the data are often ambiguous, making reconstructions based on them unusable for practical purposes. Deep learning’s ability to detect subtleties in data that traditional models do not use make it one possible candidate for the improvement of classification of Compton camera data. We show that a suitably designed neural network can reduce false detections and misorderings of interactions, thereby improving reconstruction quality.

Tools Used: Python, Tensorflow, Slurm

Technical Report: 2021 Technical Report, Summer 2020 Technical Report, Spring 2020 Report
Preprint: www.researchsquare.com/article/rs-639221/v1
Conference Proceedings: PAMM 2021, CSCI 2021, ICDATA 2021