Department of Biomedical Engineering, Medical Image Analysis group, Eindhoven University of Technology, Eindhoven, The Netherlands.
Smart Scientific Solutions, Maastricht, The Netherlands.
Phys Med Biol. 2022 Aug 8;67(16). doi: 10.1088/1361-6560/ac8390.
In preclinical radiotherapy with kilovolt (kV) x-ray beams, accurate treatment planning is needed to improve the translation potential to clinical trials. Monte Carlo based radiation transport simulations are the gold standard to calculate the absorbed dose distribution in external beam radiotherapy. However, these simulations are notorious for their long computation time, causing a bottleneck in the workflow. Previous studies have used deep learning models to speed up these simulations for clinical megavolt (MV) beams. For kV beams, dose distributions are more affected by tissue type than for MV beams, leading to steep dose gradients. This study aims to speed up preclinical kV dose simulations by proposing a novel deep learning pipeline.A deep learning model is proposed that denoises low precision (∼10simulated particles) dose distributions to produce high precision (10simulated particles) dose distributions. To effectively denoise the steep dose gradients in preclinical kV dose distributions, the model uses the novel approach to use the low precision Monte Carlo dose calculation as well as the Monte Carlo uncertainty (MCU) map and the mass density map as additional input channels. The model was trained on a large synthetic dataset and tested on a real dataset with a different data distribution. To keep model inference time to a minimum, a novel method for inference optimization was developed as well.The proposed model provides dose distributions which achieve a median gamma pass rate (3%/0.3 mm) of 98% with a lower bound of 95% when compared to the high precision Monte Carlo dose distributions from the test set, which represents a different dataset distribution than the training set. Using the proposed model together with the novel inference optimization method, the total computation time was reduced from approximately 45 min to less than six seconds on average.This study presents the first model that can denoise preclinical kV instead of clinical MV Monte Carlo dose distributions. This was achieved by using the MCU and mass density maps as additional model inputs. Additionally, this study shows that training such a model on a synthetic dataset is not only a viable option, but even increases the generalization of the model compared to training on real data due to the sheer size and variety of the synthetic dataset. The application of this model will enable speeding up treatment plan optimization in the preclinical workflow.
在使用千伏 (kV) X 射线的临床前放射治疗中，需要精确的治疗计划来提高向临床试验的转化潜力。基于蒙特卡罗的放射传输模拟是计算外束放射治疗中吸收剂量分布的金标准。然而，这些模拟因其计算时间长而臭名昭著，导致工作流程出现瓶颈。以前的研究已经使用深度学习模型来加速这些用于临床兆伏 (MV) 射线的模拟。对于 kV 射线，剂量分布比 MV 射线受组织类型的影响更大，导致剂量梯度陡峭。本研究旨在通过提出一种新的深度学习管道来加速临床前 kV 剂量模拟。
提出了一种深度学习模型，该模型对低精度（~10 个模拟粒子）剂量分布进行去噪以产生高精度（10 个模拟粒子）剂量分布。为了有效地对临床前 kV 剂量分布中的陡峭剂量梯度进行去噪，该模型使用了一种新颖的方法，即将低精度蒙特卡罗剂量计算以及蒙特卡罗不确定性 (MCU) 图和质量密度图用作附加输入通道。该模型在大型合成数据集上进行了训练，并在具有不同数据分布的真实数据集上进行了测试。为了将模型推断时间保持在最短，还开发了一种新的推断优化方法。
所提出的模型提供的剂量分布与测试集中的高精度蒙特卡罗剂量分布相比，实现了 98%的中位数伽马通过率（3%/0.3mm），下限为 95%，这代表了与训练集不同的数据分布。使用所提出的模型和新的推断优化方法，总计算时间从大约 45 分钟平均减少到不到六秒。
本研究提出了第一个可以对临床前 kV 而不是临床 MV 蒙特卡罗剂量分布进行去噪的模型。这是通过使用 MCU 和质量密度图作为附加模型输入来实现的。此外，本研究表明，在合成数据集上训练这样的模型不仅是一种可行的选择，而且由于合成数据集的规模和多样性，甚至比在真实数据上训练更能提高模型的泛化能力。该模型的应用将能够加快临床前工作流程中的治疗计划优化。
