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Microbeam radiation therapy (MRT) utilizes coplanar synchrotron radiation beamlets and is a proposed treatment approach for several tumour diagnoses that currently have poor clinical treatment outcomes, such as gliosarcomas. Prescription dose estimations for treating preclinical gliosarcoma models in MRT studies at the Imaging and Medical Beamline at the Australian Synchrotron currently rely on Monte Carlo (MC) simulations. The steep dose gradients associated with the 50$\,\mu$m wide coplanar beamlets present a significant challenge for precise MC simulation of the MRT irradiation treatment field in a short time frame. Much research has been conducted on fast dose estimation methods for clinically available treatments. However, such methods, including GPU Monte Carlo implementations and machine learning (ML) models, are unavailable for novel and emerging cancer radiation treatment options like MRT. In this work, the successful application of a fast and accurate machine learning dose prediction model in a retrospective preclinical MRT rodent study is presented for the first time. The ML model predicts the peak doses in the path of the microbeams and the valley doses between them, delivered to the gliosarcoma in rodent patients. The predictions of the ML model show excellent agreement with low-noise MC simulations, especially within the investigated tumour volume. This agreement is despite the ML model being deliberately trained with MC-calculated samples exhibiting significantly higher statistical uncertainties. The successful use of high-noise training set data samples, which are much faster to generate, encourages and accelerates the transfer of the ML model to different treatment modalities for other future applications in novel radiation cancer therapies.

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., Comment: accepted for publication in the IFMBE Proceedings on the World Congress on Medical Physics and Biomedical Engineering 2022

Novel radiotherapy techniques like synchrotron X-ray microbeam radiation therapy (MRT), require fast dose distribution predictions that are accurate at the sub-mm level, especially close to tissue/bone/air interfaces. Monte Carlo physics simulations are recognised to be one of the most accurate tools to predict the dose delivered in a target tissue but can be very time consuming and therefore prohibitive for treatment planning. Faster dose prediction algorithms are usually developed for clinically deployed treatments only. In this work, we explore a new approach for fast and accurate dose estimations suitable for novel treatments using digital phantoms used in pre-clinical development and modern machine learning techniques. We develop a generative adversarial network (GAN) model, which is able to emulate the equivalent Geant4 Monte Carlo simulation with adequate accuracy, and use it to predict the radiation dose delivered by a broad synchrotron beam to various phantoms. The energy depositions used for the training of the GAN are obtained using full Geant4 Monte Carlo simulations of a synchrotron radiation broad beam passing through the phantoms. The energy deposition is scored and predicted in voxel matrices of size 140x18x18 with a voxel edge length of 1 mm. The GAN model consists of two competing 3D convolutional neural networks, which are conditioned on the photon beam and phantom properties. The energy deposition predictions inside all phantom geometries under investigation show deviations of less than 3% of the maximum deposited energy from the simulation for roughly 99% of the voxels in the field of the beam. The computing time for a single prediction is reduced from several hundred hours using Geant4 simulation to less than a second using the GAN model., Comment: Accepted for publication in Medical Physics