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 $D_5$ 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.