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

基本信息

批准号：
10617647
负责人：
Lei Xing
金额：
$ 42.42万
依托单位：
STANFORD UNIVERSITY
依托单位国家：
美国
项目类别：
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/10617647
关键词：
3-Dimensional Affect Brain Clinical Complication Data Data Set Detection Development Discipline Disease Dose Duodenum Head and neck structure Hemorrhage Image Implant Infection Intensity-Modulated Radiotherapy Investigation Lead Learning Liver Location Lung Malignant Neoplasms Malignant neoplasm of pancreas Methods Modeling Modernization Modification Monitor Motion Nature Neoplasms Normal tissue morphology Organ Pancreas Patient Care Patients Performance Positioning Attribute Probability Procedures Process Prostate Prostate Cancer therapy Radiation Dose Unit Radiation Oncology Radiation therapy Radiosurgery Research Retrospective Studies Roentgen Rays Site System Techniques Time Training Uncertainty Vertebral column Visualization X-Ray Computed Tomography X-Ray Medical Imaging 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

项目摘要

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.

利用深度学习用于放射治疗中的无标记运动管理项目摘要器官运动是最大程度利用现代放射治疗的主要限制因素（RT）。器官运动的不利影响因降量治疗而加剧剂量递送。当前的图像引导的RT通常依赖于使用植入的基准标记（FMS）用于在线/离线目标本地化，这是侵入性和昂贵的，并引入了可能的患者出血，感染和不适。在这个项目中，我们利用了深度学习和研究一种新颖的无标记本地化策略，通过联合使用预训练深度学习模型和KV X射线投影或锥束CT图像。我们假设该合并图像信息的深层层次使我们能够实时可视化其他看不见的目标减少光束靶向的不确定性。该项目的具体目的是：（1）开发基于DL的图像引导RT（IGRT）的肿瘤目标定位框架；（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；（3）评估潜力 DL策略对胰腺IGRT的临床影响。这项研究首次提出基于深度学习的准确无标记目标定位，并提供临床上明智的解决方案对于前列腺和胰腺癌或其他类型的癌症的IGRT。成功完成调查将大大提高当前光束靶向技术并提供辐射肿瘤学纪律是一种安全可靠地升级辐射剂量的有力方法。鉴于其巨大的承诺能够最佳地迎合划分间和分数的不确定性，因此该研究应该导致患者护理的重大改善，并使我们能够最大程度地利用技术 IMRT和VMAT等现代RT的能力。考虑到各种癌症的剂量响应性质提出的方法不需要修改硬件，这项研究应导致广泛影响关于受器官运动影响的肿瘤疾病的管理。