Large-Scale Optimizations in Proton Beam Radiotherapy by Neural Network Denoising of Robust Simulated Patient Data

Abstract

Proton beam radiotherapy is an advanced cancer treatment utilizing high-energy protons to destroy tumor matter. This treatment requires precise Bragg-peak localization, but Compton-camera image reconstructions are often unusable due to mischaracterized scattering sequences and excessive image noise. We present machine learning models to classify the scattering events. Multiple novel robust-volume datasets simulating particle interactions with human tissue were generated using Duke University CT scans and Geant4 and Monte-Carlo Detector Effects (MCDE) software. Novel implementations of an Event Classifier Transformer and a 1D Convolutional Neural Network (CNN) were developed, while prior models, such as Fully-Connected Neural Networks (FCN) and Long Short-Term Memory Neural Network (LSTM), were optimized through largescale hyperparameter studies using a novel automated tuning framework built into the Big-Data REU Integrated Development and Experimentation (BRIDE) machine learning pipeline. Fullyconnected neural networks and convolutional neural networks show significant improvements in model accuracy over prior work on simulated patient data and demonstrate that relatively shallow, regularized models generalize best.