A Bayesian Inverse Approach to Proton Therapy Dose Delivery Verification

2023/11/15 by Alexander M. G. Cox, Cox, Alexander M. G., Laura Hattam +5 · 3 citations
Physics and Astronomy · Biochemistry, Genetics and Molecular Biology · #Nuclear Physics and Applications #Spectroscopy Techniques in Biomedical and Chemical Research #Radiation Detection and Scintillator Technologies

paper · pdf · doi:10.48550/arxiv.2311.10769

Abstract

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-γ'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.

Cited by

• Geometry, Energy and Sensitivity in Stochastic Proton Dynamics
• Surrogate Modelling of Proton Dose with Monte Carlo Dropout Uncertainty Quantification
• A Unified Framework from Boltzmann Transport to Proton Treatment Planning

Related

• Feasibility study of patient‐specific dose verification in proton therapy utilizing positron emission tomography (PET) and generative adversarial network (GAN)
• BayesDose: Comprehensive proton dose prediction with model uncertainty using Bayesian LSTMs
• Sensitivity analysis of biological washout and depth selection for a machine learning based dose verification framework in proton therapy
• Beam Energy Measurement using a Bayesian Approach with the Stacked Foil Method
• Development of array‐type prompt gamma measurement system for in vivo range verification in proton therapy