Deep TL: progress of a machine learning aided personal dose monitoring system.

Personal dosemeters using thermoluminescence detectors can provide information about the irradiation event beyond the pure dose estimation, which is valuable for improving radiation protection measures. In the presented study, the glow curves of the novel TL-DOS dosemeters developed by the Materialprüfungsamt NRW in cooperation with the TU Dortmund University are analysed using deep learning approaches to predict the irradiation date of a single-dose irradiation of 10 mGy within a monitoring interval of 41 d. In contrast of previous work, the glow curves are measured using the current routine read-out process by pre-heating the detectors before the read-out. The irradiation dates are predicted with an accuracy of 2-5 d by the deep learning algorithm. Furthermore, the importance of the input features is evaluated using Shapley values to increase the interpretability of the neural network.

Accurate and Fast Deep Learning Dose Prediction for a Preclinical Microbeam Radiation Therapy Study Using Low-Statistics Monte Carlo Simulations.

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.

Fast and accurate dose predictions for novel radiotherapy treatments in heterogeneous phantoms using conditional 3D-UNet generative adversarial networks.

PURPOSE: 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 (MC) physics simulations are recognized 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 preclinical development and modern machine learning techniques. We develop a generative adversarial network (GAN) model, which is able to emulate the equivalent Geant4 MC simulation with adequate accuracy and use it to predict the radiation dose delivered by a broad synchrotron beam to various phantoms. METHODS: The energy depositions used for the training of the GAN are obtained using full Geant4 MC simulations of a synchrotron radiation broad beam passing through the phantoms. The energy deposition is scored and predicted in voxel matrices of size 140 × 18 × 18 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 generator network has a U-Net structure and is designed to predict the energy depositions of the photon beam inside three phantoms of variable geometry with increasing complexity. The critic network is a relatively simple convolutional network, which is trained to distinguish energy depositions predicted by the generator from the ones obtained with the full MC simulation. RESULTS: 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. Inside the most realistic phantom, a simple pediatric head, the model predictions deviate by less than 1% of the maximal energy deposition from the simulations in more than 96% of the in-field voxels. For all three phantoms, the model generalizes the energy deposition predictions well to phantom geometries, which have not been used for training the model but are interpolations of the training data in multiple dimensions. 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. CONCLUSIONS: The proposed GAN model predicts dose distributions inside unknown phantoms with only small deviations from the full MC simulation with computations times of less than a second. It demonstrates good interpolation ability to unseen but similar phantom geometries and is flexible enough to be trained on data with different radiation scenarios without the need for optimization of the model parameter. This proof-of-concept encourages to apply and further develop the model for the use in MRT treatment planning, which requires fast and accurate predictions with sub-mm resolutions.

Enhancement of Proton Therapy Efficiency by Noble Metal Nanoparticles Is Driven by the Number and Chemical Activity of Surface Atoms.

Proton-based radiotherapy is a modern technique for the treatment of solid tumors with significantly reduced side effects to adjacent tissues. Biocompatible nanoparticles (NPs) with high atomic numbers are known to serve as sensitizers and to enhance treatment efficacy, which is commonly believed to be attributed to the generation of reactive oxygen species (ROS). However, little systematic knowledge is available on how either physical effects due to secondary electron generation or the particle surface chemistry affect ROS production. Thereto, ligand-free colloidal platinum (Pt) and gold (Au) NPs with well-controlled particle size distributions and defined total surface area are proton-irradiated. A fluorescence-based assay is developed to monitor the formation of ROS using terephthalic acid as a cross-effect-free dye. The findings indicate that proton irradiation (PI)-induced ROS formation sensitized by noble metal NPs is driven by the total available particle surface area rather than particle size or mass. Furthermore, a distinctive material effect with Pt being more active than Au is observed which clearly indicates that the chemical reactivity of the NP surface is a main contributor to ROS generation upon PI. These results pave the way towards an in-depth understanding of the NP-induced sensitizing effects upon PI and hence a well-controlled enhanced therapy.