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

• 批准号: 10374171
• 负责人: Lei Xing
• 金额: $ 42.42万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-04-01 至 2026-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10374171
• 关键词: 3-Dimensional; Affect; Brain; Clinical; Complication; Data; Data Set; Detection; Development; Discipline; Disease; Dose; Duodenum; Felis catus; Head and neck structure; Hemorrhage; Image; Implant; Infection; Intensity-Modulated Radiotherapy; Investigation; Lead; Learning; Liver; Location; Lung; Malignant Neoplasms; Malignant neoplasm of pancreas; Malignant neoplasm of prostate; Methods; Modeling; Modernization; Modification; Monitor; Motion; Nature; Neoplasms; Normal tissue morphology; Organ; Pancreas; Patient Care; Patients; Performance; Positioning Attribute; Probability; Procedures; Process; Prostate; Radiation Dose Unit; Radiation Oncology; Radiation therapy; Radiosurgery; Research; Retrospective Studies; Roentgen Rays; Site; System; Techniques; Time; Training; Uncertainty; Vertebral column; X-Ray Computed Tomography; X-Ray Medical Imaging; base; cancer type; cone-beam computed tomography; conventional therapy; convolutional neural network; cost; deep learning; deep learning algorithm; deep learning model; experimental study; image guided; image guided intervention; image guided radiation therapy; improved; indexing; learning strategy; novel; pancreas imaging; pancreas radiation therapy; predictive modeling; real time model; respiratory; treatment planning; tumor

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.

利用深度学习进行放射治疗中的无标记运动管理 项目概要 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，由于以下原因加剧了器官运动的不利影响： 延长剂量输送。当前的图像引导 RT 通常依赖于植入基准标记的使用 （FM）用于在线/离线目标定位，这是侵入性且成本高昂的，并引入了可能的 出血、感染和患者不适。在这个项目中，我们利用了巨大的潜力 深度学习并通过结合使用预训练的方法研究一种新颖的无标记定位策略 深度学习模型和 kV X 射线投影或锥束 CT 图像。我们假设合并 深层图像信息使我们能够实时且极大地可视化原本看不见的目标 减少波束瞄准的不确定性。该项目的具体目标是： (1) 开发基于 DL 的 图像引导 RT (IGRT) 的肿瘤靶点定位框架； (2) 应用基于DL的策略 在 2D kV X 射线投影和 3D CBCT 图像上定位前列腺目标； (3) 评估潜力 DL 策略对胰腺 IGRT 的临床影响。这项研究首次提出高度 基于深度学习的精确无标记目标定位，并提供临床上合理的解决方案 用于前列腺癌和胰腺癌或其他类型癌症的 IGRT。顺利完成本次 研究将显着推进当前的光束瞄准技术并提供辐射 肿瘤学学科是一种安全可靠地增加精确放疗辐射剂量的强大方法。 鉴于其对最佳地满足分数间和分数内不确定性的重大承诺，该研究 应该会显着改善患者护理并使我们能够最大限度地利用技术 现代放疗（例如 IMRT 和 VMAT）的能力。鉴于各种癌症的剂量反应性质 由于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 关于受器官运动影响的肿瘤疾病的治疗。