A step towards treatment planning for microbeam radiation therapy: fast peak and valley dose predictions with 3D U-Nets

TL;DR

This paper introduces a fast machine learning approach using 3D U-Nets to predict macro peak and valley doses in microbeam radiation therapy, significantly speeding up treatment planning while maintaining accuracy.

Contribution

The study presents a novel macro dose distribution model with 3D U-Nets that predicts microbeam radiation fields efficiently, incorporating complex physical effects automatically.

Findings

Predictions made in less than a minute.

Maintains reasonable accuracy compared to Monte Carlo simulations.

Enables superimposing microbeam predictions for treatment planning.

Abstract

Fast and accurate dose predictions are one of the bottlenecks in treatment planning for microbeam radiation therapy (MRT). In this paper, we propose a machine learning (ML) model based on a 3D U-Net. Our approach predicts separately the large doses of the narrow high intensity synchrotron microbeams and the lower valley doses between them. For this purpose, a concept of macro peak doses and macro valley doses is introduced, describing the respective doses not on a microscopic level but as macroscopic quantities in larger voxels. The ML model is trained to mimic full Monte Carlo (MC) data. Complex physical effects such as polarization are therefore automatically taking into account by the model. The macro dose distribution approach described in this study allows for superimposing single microbeam predictions to a beam array field making it an interesting candidate for treatment planning. It is shown that the proposed approach can overcome a main obstacle with microbeam dose predictions by predicting a full microbeam irradiation field in less than a minute while maintaining reasonable accuracy.