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Microbeam radiation therapy (MRT) has shown several advantages compared with conventional broad-beam radiation therapy in small animal models, including a better preservation of normal tissue function and improved drug delivery based on a rapidly increased vascular permeability in the target region. Normal tissue tolerance is the limiting factor in clinical radiation therapy. Knowledge of the normal tissue tolerance of organs at risk is therefore a prerequisite in evaluating any new radiation therapy approach. With an irradiation target in the thoracic cavity, the heart would be the most important organ at risk.We used the ex vivo beating rodent heart in the Langendorff perfusion system at the synchrotron to administer microbeam irradiation (MBI) with a peak dose of 40 or 400 Gy. By continuously recording the electrocardiogram, the left ventricular pressure, and the aortic pressure before, during and after MBI, we were able to assess acute and subacute effects of MBI on electrophysiological and mechanical cardiac function. In addition, we analyzed histologic and ultrastructural sequelae caused by MBI.There were no significant changes in heart rate, heart rate variability, systolic increase of left ventricular pressure or aortic pressure. Moreover, the changes of heart rate, left ventricular pressure and aortic pressure by adding 10With respect to acute and subacute toxicity, an MBI peak dose up to 400 Gy did not result in severe changes in cardiac electrophysiology or mechanics.

This work describes the creation and experimental validation of DoseMRT, a new software package, and its associated workflow for dose calculations in synchrotron-generated broad beam and microbeam radiation treatment fields. The DoseMRT software package allows users to import CT DICOM datasets into Geant4 for Monte Carlo dose calculations. It also provides basic treatment planning capabilities, simplifying the complexity of performing Geant4 simulations and making our Monte Carlo dose calculation algorithm accessible to a broader range of users. To demonstrate the new package, dose calculations are validated against experimental measurements performed in homogeneous water tank phantoms and the anatomically complex Alderson Radiotherapy Phantom for both broad-beam and microbeam configurations. Additionally, DoseMRT is successfully utilised as the primary method for patient-specific treatment prescription in an in vivo experiment involving tumour-bearing rats at the Imaging and Medical Beamline of the Australian Synchrotron.

Microbeam radiation therapy (MRT) utilizes coplanar synchrotron radiation beamlets and is a proposed treatment approach for several tumor diagnoses that currently have poor clinical treatment outcomes, such as gliosarcomas. Monte Carlo (MC) simulations are one of the most used methods at the Imaging and Medical Beamline, Australian Synchrotron to calculate the dose in MRT preclinical studies. The steep dose gradients associated with the 50μm-wide coplanar beamlets present a significant challenge for precise MC simulation of the dose deposition of an MRT irradiation treatment field in a short time frame. The long computation times inhibit the ability to perform dose optimization in treatment planning or apply online image-adaptive radiotherapy techniques to MRT. 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 radiotherapy options such as MRT. In this work, the successful application of a fast and accurate ML dose prediction model for a 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 tumor target in rat patients. A CT imaging dataset is used to generate digital phantoms for each patient. Augmented variations of the digital phantoms are used to simulate with Geant4 the energy depositions of an MRT beam inside the phantoms with 15% (high-noise) and 2% (low-noise) statistical uncertainty. The high-noise MC simulation data are used to train the ML model to predict the energy depositions in the digital phantoms. The low-noise MC simulations data are used to test the predictive power of the ML model. The predictions of the ML model show an agreement within 3% with low-noise MC simulations for at least 77.6% of all predicted voxels (at least 95.9% of voxels containing tumor) in the case of the valley dose prediction and for at least 93.9% of all predicted voxels (100.0% of voxels containing tumor) in the case of the peak dose prediction. The successful use of high-noise MC simulations for the training, which are much faster to produce, accelerates the production of the training data of the ML model and encourages transfer of the ML model to different treatment modalities for other future applications in novel radiation cancer therapies.

Microbeam radiotherapy (MRT), a high dose rate radiotherapy technique using spatial dose fractionation at the micrometre range, has shown a high therapeutic efficacy in vivo in different tumour entities, including lung cancer. We have conducted a toxicity study for the spinal cord as organ of risk during irradiation of a target in the thoracic cavity. In young adult rats, the lower thoracic spinal cord was irradiated over a length of 2 cm with an array of quasi-parallel microbeams of 50 µm width, spaced at a centre-to-centre distance of 400 µm, with MRT peak doses up to 800 Gy. No acute or subacute adverse effects were observed within the first week after irradiation up to MRT peak doses of 400 Gy. No significant differences were seen between irradiated animals and non-irradiated controls in motor function and sensitivity, open field test and somatosensory evoked potentials (SSEP). After irradiation with MRT peak doses of 450–800 Gy, dose-dependent neurologic signs occurred. Provided that long-term studies do not reveal significant morbidity due to late toxicity, an MRT dose of 400 Gy can be considered safe for the spinal cord in the tested beam geometry and field size.

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