Kato Ryohei, Kadoya Noriyuki, Kato Takahiro, Tozuka Ryota, Ogawa Shuta, Murakami Masao, Jingu Keiichi
Department of Radiation Physics and Technology, Southern Tohoku Proton Therapy Center, 7-172 Yatsuyamada, Koriyama, Fukushima, 963-8052, Japan.
Department of Radiation Oncology, Tohoku University Graduate School of Medicine, 1-1 Seiryou-machi, Aoba-ku, Sendai, Miyagi, 980-8574, Japan.
J Radiat Res. 2025 May 23;66(3):280-289. doi: 10.1093/jrr/rraf019.
This study is aimed to clarify the effectiveness of the image-rotation technique and zooming augmentation to improve the accuracy of the deep learning (DL)-based dose conversion from pencil beam (PB) to Monte Carlo (MC) in proton beam therapy (PBT). We adapted 85 patients with head and neck cancers. The patient dataset was randomly divided into 101 plans (334 beams) for training/validation and 11 plans (34 beams) for testing. Further, we trained a DL model that inputs a computed tomography (CT) image and the PB dose in a single-proton field and outputs the MC dose, applying the image-rotation technique and zooming augmentation. We evaluated the DL-based dose conversion accuracy in a single-proton field. The average γ-passing rates (a criterion of 3%/3 mm) were 80.6 ± 6.6% for the PB dose, 87.6 ± 6.0% for the baseline model, 92.1 ± 4.7% for the image-rotation model, and 93.0 ± 5.2% for the data-augmentation model, respectively. Moreover, the average range differences for R90 were - 1.5 ± 3.6% in the PB dose, 0.2 ± 2.3% in the baseline model, -0.5 ± 1.2% in the image-rotation model, and - 0.5 ± 1.1% in the data-augmentation model, respectively. The doses as well as ranges were improved by the image-rotation technique and zooming augmentation. The image-rotation technique and zooming augmentation greatly improved the DL-based dose conversion accuracy from the PB to the MC. These techniques can be powerful tools for improving the DL-based dose calculation accuracy in PBT.
本研究旨在阐明图像旋转技术和缩放增强在提高质子束治疗（PBT）中基于深度学习（DL）的从笔形束（PB）到蒙特卡罗（MC）剂量转换准确性方面的有效性。我们纳入了85例头颈部癌患者。将患者数据集随机分为用于训练/验证的101个计划（334束）和用于测试的11个计划（34束）。此外，我们训练了一个DL模型，该模型输入单质子场中的计算机断层扫描（CT）图像和PB剂量，并输出MC剂量，同时应用图像旋转技术和缩放增强。我们评估了单质子场中基于DL的剂量转换准确性。对于PB剂量，平均γ通过率（标准为3%/3 mm）为80.6±6.6%，基线模型为87.6±6.0%，图像旋转模型为92.1±4.7%，数据增强模型为93.0±5.2%。此外，R90的平均射程差异在PB剂量中为-1.5±3.6%，基线模型中为0.2±2.3%，图像旋转模型中为-0.5±1.2%，数据增强模型中为-0.5±1.1%。图像旋转技术和缩放增强改善了剂量以及射程。图像旋转技术和缩放增强极大地提高了基于DL的从PB到MC的剂量转换准确性。这些技术可以成为提高PBT中基于DL的剂量计算准确性的有力工具。
