Jia Mengyu, Li Xiaomeng, Wu Yan, Yang Yong, Kasimbeg Priya, Skinner Lawrie, Wang Lei, Xing Lei
Department of Radiation Oncology, Stanford University, Palo Alto 94304, United States of America.
School of Engineering, Stanford University, Palo Alto 94304, United States of America.
Phys Med Biol. 2021 Feb 9;66(4):045014. doi: 10.1088/1361-6560/abd673.
This study aims to demonstrate a low-cost camera-based radioluminescence imaging system (CRIS) for high-quality beam visualization that encourages accurate pre-treatment verifications on radiation delivery in external beam radiotherapy. To ameliorate the optical image that suffers from mirror glare and edge blurring caused by photon scattering, a deep learning model is proposed and trained to learn from an on-board electronic portal imaging device (EPID). Beyond the typical purposes of an on-board EPID, the developed system maintains independent measurement with co-planar detection ability by involving a cylindrical receptor. Three task-aware modules are integrated into the network design to enhance its robustness against the artifacts that exist in an EPID running at the cine mode for efficient image acquisition. The training data consists of various designed beam fields that were modulated via the multi-leaf collimator (MLC). Validation experiments are performed for five regular fields ranging from 2 × 2 cm to 10 × 10 cm and three clinical IMRT cases. The captured CRIS images are compared to the high-quality images collected from an EPID running at the integration-mode, in terms of gamma index and other typical similarity metrics. The mean 2%/2 mm gamma pass rate is 99.14% (range between 98.6% and 100%) and 97.1% (ranging between 96.3% and 97.9%), for the regular fields and IMRT cases, respectively. The CRIS is further applied as a tool for MLC leaf-end position verification. A rectangular field with introduced leaf displacement is designed, and the measurements using CRIS and EPID agree within 0.100 mm ± 0.072 mm with maximum of 0.292 mm. Coupled with its simple system design and low-cost nature, the technique promises to provide viable choice for routine quality assurance in radiation oncology practice.
本研究旨在展示一种基于低成本相机的放射发光成像系统（CRIS），用于高质量束流可视化，以促进对外照射放疗中放射治疗剂量传递的准确治疗前验证。为了改善因光子散射导致的镜面眩光和边缘模糊的光学图像，提出并训练了一种深度学习模型，以从机载电子射野影像装置（EPID）中学习。除了机载EPID的典型用途外，所开发的系统通过采用圆柱形接收器，保持了具有共面检测能力的独立测量。三个任务感知模块被集成到网络设计中，以增强其对在电影模式下运行的EPID中存在的伪影的鲁棒性，从而实现高效的图像采集。训练数据由通过多叶准直器（MLC）调制的各种设计射野组成。对五个尺寸范围从2×2 cm到10×10 cm的常规射野和三个临床调强放疗（IMRT）病例进行了验证实验。将捕获的CRIS图像与从以积分模式运行的EPID收集的高质量图像在伽马指数和其他典型相似性指标方面进行比较。对于常规射野和IMRT病例，平均2%/2 mm伽马通过率分别为99.14%（范围在98.6%至100%之间）和97.1%（范围在96.3%至97.9%之间）。CRIS进一步用作MLC叶片末端位置验证的工具。设计了一个引入叶片位移的矩形射野，使用CRIS和EPID进行的测量结果在0.100 mm±0.072 mm范围内一致，最大差值为0.292 mm。结合其简单的系统设计和低成本特性，该技术有望为放射肿瘤学实践中的常规质量保证提供可行的选择。
