Radiation dose calculation in 3D heterogeneous media using artificial neural networks. Issue 5 (16th March 2021)

• Record Type: Journal Article
• Authors: Keal, James; Santos, Alexandre; Penfold, Scott; Douglass, Michael
• Is Part Of: Medical physics. Volume 48:Issue 5(2021)
• Page Start: 2637
• Page End: 2645
• Publication Date: 2021-03-16
• Subjects: artificial intelligence -- deep learning -- dose calculation -- dosimetry -- neural network -- radiotherapy; Medical physics -- Periodicals; Medical physics; Geneeskunde; Natuurkunde; Toepassingen; Biophysics; Periodicals; Periodicals; Electronic journals; 610.153
• DOI: 10.1002/mp.14780
• Languages: English
• ISSNs: 0094-2405

Abstract

Abstract : Purpose: External beam radiotherapy (EBRT) treatment planning requires a fast and accurate method of calculating the dose delivered by a clinical treatment plan. However, existing methods of calculating dose distributions have limitations. Monte Carlo (MC) methods are accurate but can take too long to be clinically viable. Deterministic approaches are quicker but can be inaccurate under certain conditions, particularly near heterogeneities and air interfaces. Neural networks trained on MC‐derived data have the potential to reproduce dose distributions that agree closely with the MC method while being significantly quicker to deploy. Methods: In this work we present a framework for training machine learning models capable of directly calculating the dose delivered to a point in three‐dimensional (3D) heterogeneous media given only spatially local information. The framework consists of three parts. First, we describe a novel method of randomly generating 3D heterogeneous geometries using simplex noise. Dose distributions for training were obtained by importing these geometries into a MC simulation. The second and third parts of the framework are precalculated data channels, aligned with the patient computed tomography (CT) image, to be used as input to the model. These data channels are a computationally efficient way of encoding the parameters of an incident radiation beam while also allowing the model to learn from data that would otherwise be outside of its receptive field. Results: We demonstrate the viability of the framework by a training small, fully connected neural network model to reproduce dose distributions from megavoltage photon beams. The trained network displayed excellent agreement with MC dose distributions in randomly generated geometries with an average gamma index (3%/3 mm) pass rate of 94.7% and an average error of 1.45% of peak dose. Finally, the network was used to calculate the dose in a patient CT image, on which the network was not trained, producing similarly impressive results. Conclusions: A novel method of generating training data for learned radiation dosimetry models has been introduced, along with preprocessing steps that allow even simple models to reproduce accurate dose distributions for EBRT. More importantly, we have demonstrated that a model trained using the proposed framework can generalize from the training data to predicting the therapeutic dose in realistic media.