This study presents a proof-of-concept for a novel Bayesian inverse method in a one-dimensional setting, aimed at proton beam therapy treatment verification. Our methodology is predicated on a hypothetical scenario wherein strategically positioned sensors detect prompt-{\gamma}'s emitted from a proton beam when it interacts with defined layers of tissue. Using this data, we employ a Bayesian framework to estimate the proton beam's energy deposition profile. We validate our Bayesian inverse estimations against a closed-form approximation of the Bragg Peak in a uniform medium and a layered lung tumour.
@article{arxiv.2311.10769,
title = {A Bayesian Inverse Approach to Proton Therapy Dose Delivery Verification},
author = {Alexander M. G. Cox and Laura Hattam and Andreas E. Kyprianou and Tristan Pryer},
journal= {arXiv preprint arXiv:2311.10769},
year = {2023}
}