A Bayesian inverse approach to proton therapy dose delivery verification

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- <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"> <mml:mi>γ</mml:mi> </mml:math> s emitted from a proton beam when it interacts with defined layers of tissue. Using these 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.