Leveraging deep learning for markerless motion management in radiation therapy

利用深度学习进行放射治疗中的无标记运动管理 项目摘要 器官运动是最大限度地利用现代放射治疗的主要限制因素 （RT）。在大分割治疗中，器官运动的不良影响加重， 延长剂量递送。当前的图像引导RT通常依赖于植入的基准标记的使用 (FMs)用于在线/离线目标定位，这是侵入性的和昂贵的，并且引入了可能的 出血、感染和患者不适。在这个项目中，我们利用了 深度学习和研究一种新的无标记定位策略， 深度学习模型和kV X射线投影或锥形束CT图像。我们假设合并 图像信息的深层使我们能够实时地、极大地可视化原本不可见的目标， 减少射束瞄准的不确定性。本课题的具体目标是：（1）开发基于DL的 图像引导RT（IGRT）的肿瘤靶点定位框架;（2）将基于DL的策略应用于 在2D kV X射线投影和3D CBCT图像上定位前列腺靶点;（3）评估潜在的 DL策略对胰腺IGRT的临床影响。这项研究第一次提出， 基于深度学习的精确无标记目标定位，并提供临床合理的解决方案 用于前列腺癌和胰腺癌或其他类型癌症的IGRT。成功完成本 这项研究将大大推进目前的射束瞄准技术， 肿瘤学科的一个强大的方式来安全可靠地逐步增加辐射剂量的精确RT。 鉴于其在最佳地满足部分间和部分内不确定性方面的重要承诺， 应该导致病人护理的实质性改善，使我们能够最大限度地利用技术， 现代RT的能力，如IMRT和VMAT。鉴于各种癌症的剂量反应性质， 所提出的方法不需要硬件修改，这项研究应该会产生广泛的影响。 对受器官运动影响的肿瘤疾病的管理。

Leveraging deep learning for markerless motion management in radiation therapy Project Summary Organ motion is a predominant limiting factor for the maximum exploitation of modern radiation therapy (RT). Adverse influence of the organ motion is aggravated in hypofractionated treatment because of protracted dose delivery. Current image guided RT often relies on the use of implanted fiducial markers (FMs) for online/offline target localization, which is invasive and costly, and introduces possible bleeding, infection and discomfort of the patient. In this project, we harness the enormous potential of deep learning and investigate a novel markerless localization strategy by combined use of a pre-trained deep learning model and kV X-ray projection or cone beam CT images. We hypothesize that incorporation of deep layers of image information allows us to visualize otherwise invisible target in real-time and greatly reduce the uncertainties in beam targeting. Specific aims of the project are to: (1) Develop a DL-based tumor target localization framework for image guided RT (IGRT); (2) Apply the DL-based strategy to localize prostate target on 2D kV X-ray projection and 3D CBCT images; and (3) Evaluate the potential clinical impact of the DL strategy for pancreatic IGRT. This study brings up, for the first time, highly accurate markerless target localization based on deep learning and provides a clinically sensible solution for IGRT of prostate and pancreas cancers or other types of cancers. Successful completion of this investigation will significantly advance the current beam targeting technique and provide radiation oncology discipline a powerful way to safely and reliably escalate the radiation dose for precision RT. Given its significant promise to optimally cater for inter- and intra-fractional uncertainties, the study should lead to substantial improvement in patient care and enables us to utilize maximally the technical capability of modern RT such as IMRT and VMAT. Given the dose responsive nature of various cancers and that the proposed method requires no hardware modification, this research should lead to a widespread impact on the management of neoplasmic diseases affected by organ motion.