Department of Medical Physics, Centre Oscar Lambret, Lille, France; Faculty of Biomedical Sciences, University of Brussels ULB, Belgium.
Department of Medical Physics, Centre Oscar Lambret, Lille, France; University of Lille, CNRS, CRIStAL, Centrale Lille, France.
Phys Med. 2020 Feb;70:123-132. doi: 10.1016/j.ejmp.2020.01.018. Epub 2020 Jan 30.
This work describes the integration of the M6 Cyberknife in the Moderato Monte Carlo platform, and introduces a machine learning method to accelerate the modelling of a linac.
The MLC-equipped M6 Cyberknife was modelled and integrated in Moderato, our in-house platform offering independent verification of radiotherapy dose distributions. The model was validated by comparing TPS dose distributions with Moderato and by film measurements. Using this model, a machine learning algorithm was trained to find electron beam parameters for other M6 devices, by simulating dose curves with varying spot size and energy. The algorithm was optimized using cross-validation and tested with measurements from other institutions equipped with a M6 Cyberknife.
Optimal agreement in the Monte Carlo model was reached for a monoenergetic electron beam of 6.75 MeV with Gaussian spatial distribution of 2.4 mm FWHM. Clinical plan dose distributions from Moderato agreed within 2% with the TPS, and film measurements confirmed the accuracy of the model. Cross-validation of the prediction algorithm produced mean absolute errors of 0.1 MeV and 0.3 mm for beam energy and spot size respectively. Prediction-based simulated dose curves for other centres agreed within 3% with measurements, except for one device where differences up to 6% were detected.
The M6 Cyberknife was integrated in Moderato and validated through dose re-calculations and film measurements. The prediction algorithm was successfully applied to obtain electron beam parameters for other M6 devices. This method would prove useful to speed up modelling of new machines in Monte Carlo systems.
本工作描述了将 M6 Cyberknife 集成到 Moderato 蒙特卡罗平台中，并介绍了一种机器学习方法，以加速直线加速器的建模。
对配备 MLC 的 M6 Cyberknife 进行建模并集成到 Moderato 中，Moderato 是我们内部的平台，提供独立的放射治疗剂量分布验证。通过比较 TPS 剂量分布与 Moderato 和胶片测量，验证了模型的准确性。使用该模型，通过模拟具有不同光斑尺寸和能量的剂量曲线，训练机器学习算法来为其他 M6 设备找到电子束参数。该算法使用交叉验证进行优化，并使用配备 M6 Cyberknife 的其他机构的测量值进行测试。
对于能量为 6.75 MeV、空间分布为 2.4mm FWHM 的单能电子束，达到了蒙特卡罗模型的最佳一致性。从 Moderato 计算的临床计划剂量分布与 TPS 一致，误差在 2%以内，并且胶片测量证实了模型的准确性。预测算法的交叉验证产生了 0.1 MeV 和 0.3 mm 的平均绝对误差，分别用于光束能量和光斑尺寸。基于预测的其他中心的模拟剂量曲线与测量值之间的差异在 3%以内，除了一个设备的差异高达 6%。
将 M6 Cyberknife 集成到 Moderato 中，并通过剂量重新计算和胶片测量进行验证。预测算法成功地应用于获取其他 M6 设备的电子束参数。这种方法对于加速蒙特卡罗系统中新型机器的建模将非常有用。
