Deep learning‐based Monte Carlo dose prediction for heavy‐ion online adaptive radiotherapy and fast quality assurance: A feasibility study

Abstract Background Online adaptive radiotherapy (OART) and rapid quality assurance (QA) are essential for effective heavy ion therapy (HIT). However, there is a shortage of deep learning (DL) models and workflows for predicting Monte Carlo (MC) doses in such treatments. Purpose This study seeks to address this gap by developing a DL model for independent MC dose (MCDose) prediction, aiming to facilitate OART and rapid QA implementation for HIT. Methods and Materials A MC dose prediction DL model called CAM‐CHD U‐Net for HIT was introduced, based on the GATE/Geant4 MC simulation platform. The proposed model improved upon the original CHD U‐Net by adding a Channel Attention Mechanism (CAM). Two experiments were conducted, one with CHD U‐Net (Experiment 1) and another with CAM‐CHD U‐Net (Experiment 2), and involved data from 120 head and neck cancer patients. Using patient CT images, three‐dimensional energy matrices, and ray‐masks as inputs, the model completed the entire MC dose prediction process within a few seconds. Results In Experiment 2, within the Planned Target Volume (PTV) region, the average gamma passing rate (3%/3 mm) between the predicted dose and true MC dose reached 99.31%, and 96.48% across all body voxels. Experiment 2 demonstrated a 46.15% reduction in the mean absolute difference in in organs at risk compared to Experiment 1. Conclusions By extracting relevant parameters of radiotherapy plans, the CAM‐CHD U‐Net model can directly and accurately predict independent MC dose, and has a high gamma passing rate with the ground truth dose (the dose obtained after a complete MC simulation). Our workflow enables the implementation of heavy ion OART, and the predicted MCDose can be used for rapid QA of HIT.