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.

翻译：容积旋转调强放疗（VMAT）通过精确递送辐射剂量同时保护健康组织，彻底改变了癌症治疗。通量图生成作为VMAT计划中的关键环节，传统方法依赖复杂且耗时的迭代过程。这些通量图随后被用于多叶准直器序列优化。本文提出的深度学习方法旨在通过直接从患者数据预测通量图来加速这一过程。我们开发了一种3D网络，采用监督学习方式，结合L1和L2损失函数，以Eclipse系统及REQUITE数据集生成的放疗计划为训练数据——将放疗剂量图作为输入，对应放疗计划计算所得的通量图作为目标。我们的网络能够联合预测单弧VMAT计划中180个控制点对应的全部180张通量图。为提升网络性能，我们对输入剂量数据进行预处理：将3D剂量图投影至180个控制点的射束方向视角坐标系，该坐标系与通量图坐标系保持一致。通过Eclipse系统生成超过2000个VMAT计划以扩充数据集规模。此外，我们评估了多种网络架构，并分析了扩大数据集规模的影响。我们在验证集上使用图像质量指标（PSNR、SSIM）在二维通量图域评估性能，同时通过剂量体积直方图在三维剂量域进行分析。网络推理时间（不含数据加载与预处理）低于20毫秒。相较于在原始REQUITE数据集上训练的U-Net架构，采用我们提出的3D网络架构并结合Eclipse扩增数据集，使通量图重建性能在PSNR指标上提升约8 dB。所得剂量体积直方图与输入目标剂量高度吻合。