School of Physical Sciences, University of Adelaide, SA, 5005, Australia.
Department of Medical Physics, Royal Adelaide Hospital, SA, 5000, Australia.
Med Phys. 2021 May;48(5):2637-2645. doi: 10.1002/mp.14780. Epub 2021 Mar 16.
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.
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.
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.
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.
外束放射治疗（EBRT）治疗计划需要一种快速而准确的方法来计算临床治疗计划所给予的剂量。然而，现有的剂量分布计算方法存在局限性。蒙特卡罗（MC）方法虽然准确，但在临床应用中可能需要太长时间。确定性方法虽然速度较快，但在某些情况下可能会不准确，特别是在存在异质体和空气界面附近。基于 MC 数据训练的神经网络有可能生成与 MC 方法非常吻合的剂量分布，同时大大缩短部署时间。
在这项工作中，我们提出了一种框架，用于训练机器学习模型，该模型能够仅根据空间局部信息直接计算三维（3D）异质介质中某一点的剂量。该框架由三部分组成。首先，我们描述了一种使用单纯形噪声随机生成 3D 异质体的新方法。通过将这些几何图形导入 MC 模拟中，获得训练用的剂量分布。框架的第二和第三部分是预计算的数据通道，与患者的计算机断层扫描（CT）图像对齐，作为模型的输入。这些数据通道是一种计算效率高的方式，可以对入射辐射束的参数进行编码，同时允许模型从否则超出其感受野的数据中学习。
我们通过训练一个小型的全连接神经网络模型来重现兆伏光子束的剂量分布，证明了该框架的可行性。在随机生成的几何图形中，训练有素的网络与 MC 剂量分布显示出极好的一致性，平均伽玛指数（3%/3mm）通过率为 94.7%，峰值剂量的平均误差为 1.45%。最后，我们使用该网络计算了一个患者 CT 图像中的剂量，该网络未在该图像上进行训练，结果同样令人印象深刻。
我们提出了一种新的方法来生成用于学习辐射剂量模型的训练数据，并提出了预处理步骤，即使是简单的模型也可以重现 EBRT 的准确剂量分布。更重要的是，我们已经证明，使用所提出的框架训练的模型可以从训练数据中推广到预测真实介质中的治疗剂量。
