AUTHOR=Schellhammer Sonja M. , Meric Ilker , Löck Steffen , Kögler Toni TITLE=Hybrid treatment verification based on prompt gamma rays and fast neutrons: multivariate modelling for proton range determination JOURNAL=Frontiers in Physics VOLUME=11 YEAR=2023 URL=https://www.frontiersin.org/journals/physics/articles/10.3389/fphy.2023.1295157 DOI=10.3389/fphy.2023.1295157 ISSN=2296-424X ABSTRACT=

Robust and fast in vivo treatment verification is expected to increase the clinical efficacy of proton therapy. The combined detection of prompt gamma rays and neutrons has recently been proposed for this purpose and shown to increase the monitoring accuracy. However, the potential of this technique is not fully exploited yet since the proton range reconstruction relies only on a simple landmark of the particle production distributions. Here, we apply machine learning based feature selection and multivariate modelling to improve the range reconstruction accuracy of the system in an exemplary lung cancer case in silico. We show that the mean reconstruction error of this technique is reduced by 30%–50% to a root mean squared error per spot of 0.4, 1.0, and 1.9 mm for pencil beam scanning spot intensities of 108, 107, and 106 initial protons, respectively. The best model performance is reached when combining distribution features of both gamma rays and neutrons. This confirms the advantage of hybrid gamma/neutron imaging over a single-particle approach in the presented setup and increases the potential of this system to be applied clinically for proton therapy treatment verification.