TY - UNPB
T1 - A Bayesian Inverse Approach to Proton Therapy Dose Delivery Verification
AU - Cox, Alexander M. G.
AU - Hattam, Laura
AU - Kyprianou, Andreas E.
AU - Pryer, Tristan
N1 - 22 pages, 12 figures
PY - 2023/11/15
Y1 - 2023/11/15
N2 - 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.
AB - 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.
KW - stat.AP
M3 - Preprint
BT - A Bayesian Inverse Approach to Proton Therapy Dose Delivery Verification
PB - arXiv
ER -