Abstract
Volumetric Modulated Arc Therapy (VMAT) revolutionizes cancer treatment by precisely delivering radiation while sparing healthy tissues. Fluence maps generation, crucial in VMAT planning, traditionally involves complex and iterative, and thus time consuming processes. These fluence maps are subsequently leveraged for leaf-sequence. The deep-learning approach presented in this article aims to expedite this by directly predicting fluence maps from patient data. We developed a 3D network which we trained in a supervised way using a combination of L1 and L2 losses, and RT plans generated by Eclipse and from the REQUITE dataset, taking the RT dose map as input and the fluence maps computed from the corresponding RT plans as target. Our network predicts jointly the 180 fluence maps corresponding to the 180 control points (CP) of single arc VMAT plans. In order to help the network, we pre-process the input dose by computing the projections of the 3D dose map to the beam's eye view (BEV) of the 180 CPs, in the same coordinate system as the fluence maps. We generated over 2000 VMAT plans using Eclipse to scale up the dataset size. Additionally, we evaluated various network architectures and analyzed the impact of increasing the dataset size. We are measuring the performance in the 2D fluence maps domain using image metrics (PSNR, SSIM), as well as in the 3D dose domain using the dose-volume histogram (DVH) on a validation dataset. The network inference, which does not include the data loading and processing, is less than 20ms. Using our proposed 3D network architecture as well as increasing the dataset size using Eclipse improved the fluence map reconstruction performance by approximately 8 dB in PSNR compared to a U-Net architecture trained on the original REQUITE dataset. The resulting DVHs are very close to the one of the input target dose.
Abstract (translated)
体积调制旋转治疗（VMAT）通过精确地向肿瘤输送放射线同时保护健康组织，革新了癌症治疗方法。在VMAT规划中至关重要的射束强度分布图的生成过程传统上是复杂且迭代性的，因此耗时较长。这些射束强度分布图随后被用于叶片序列中。本文介绍的一种深度学习方法旨在通过直接从患者数据预测射束强度分布图来加快这一过程。 我们开发了一种3D网络，并使用Eclipse系统产生的和REQUITE数据集中RT计划生成的组合，采用监督式训练方式，在输入为RT剂量图、目标为从相应RT计划中计算出的射束强度分布图的情况下进行训练。我们的网络能够同时预测单弧VMAT计划中的180个控制点（CP）对应的180张射束强度分布图。为了帮助网络，我们对输入剂量进行了预处理，通过计算3D剂量图在各个控制点视图下的投影，使其与射束强度分布图使用相同的坐标系。 我们利用Eclipse系统生成了超过2000个VMAT计划以扩大数据集规模，并评估了各种网络架构以及增加数据集大小的影响。我们在二维射束强度分布图领域使用图像质量指标（PSNR和SSIM），在三维剂量域中则采用剂量体积直方图（DVH）来一个验证数据集中衡量性能。 我们的网络推理过程（不包括数据加载和处理步骤）耗时不到20毫秒。相较于在原始REQUITE数据集上训练的U-Net架构，使用我们提出的3D网络结构以及通过Eclipse系统增加数据集规模的方法使射束强度分布图重建性能提升了约8dB PSNR。生成的剂量体积直方图（DVH）与输入的目标剂量非常接近。 这种技术的进步为癌症治疗提供了更加高效且准确的方式，有助于减少患者接受放射治疗的时间和潜在风险。
URL
https://arxiv.org/abs/2502.03360
