Real-Time Volumetric Imaging for Lung Cancer Radiotherapy

肺癌放射治疗的实时体积成像

• 批准号: 8921946
• 负责人: Ruijiang Li
• 金额: $ 24.22万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-08-02 至 2017-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8921946
• 关键词: Accounting; Address; Adopted; Affect; Algorithms; Anatomy; Award; Breathing; Cancer Patient; Clinical; Data; Data Set; Dose; Future; Goals; Grouping; Image; Imaging Device; Imaging Techniques; Intervention; Knowledge; Lead; Lifting; Linear Accelerator Radiotherapy Systems; Liver; Lung; Lung Neoplasms; Malignant neoplasm of lung; Medical; Mentors; Methodology; Methods; Modeling; Morphologic artifacts; Motion; Nature; Normal tissue morphology; Outcome Study; Pancreas; Pathology; Patients; Performance; Phase; Positioning Attribute; Process; Quality of life; Radiation; Radiation therapy; Research; Research Personnel; Research Project Grants; Research Training; Respiration; Rotation; Safety; Series; Site; Statistical Models; Techniques; Therapeutic; Time; Toxic effect; Training; Universities; Work; X-Ray Computed Tomography; base; cancer radiation therapy; career; career development; clinically significant; digital; flexibility; image guided; image reconstruction; imaging modality; imaging system; improved; innovation; lung volume; medical schools; meetings; parallel computer; programs; reconstruction; research and development; respiratory; signal processing; simulation; time use; tool; treatment duration; tumor; Accounting; Address; Adopted; Affect; Algorithms; Anatomy; Award; Breathing; Cancer Patient; Clinical; Data; Data Set; Dose; Future; Goals; Grouping; Image; Imaging Device; Imaging Techniques; Intervention; Knowledge; Lead; Lifting; Linear Accelerator Radiotherapy Systems; Liver; Lung; Lung Neoplasms; Malignant neoplasm of lung; Medical; Mentors; Methodology; Methods; Modeling; Morphologic artifacts; Motion; Nature; Normal tissue morphology; Outcome Study; Pancreas; Pathology; Patients; Performance; Phase; Positioning Attribute; Process; Quality of life; Radiation; Radiation therapy; Research; Research Personnel; Research Project Grants; Research Training; Respiration; Rotation; Safety; Series; Site; Statistical Models; Techniques; Therapeutic; Time; Toxic effect; Training; Universities; Work; X-Ray Computed Tomography; base; cancer radiation therapy; career; career development; clinically significant; digital; flexibility; image guided; image reconstruction; imaging modality; imaging system; improved; innovation; lung volume; medical schools; meetings; parallel computer; programs; reconstruction; research and development; respiratory; signal processing; simulation; time use; tool; treatment duration; tumor

Interfraction anatomic changes and intrafraction respiratory motion are the major limiting factors for escalating radiation dose and improving local control in lung cancer radiotherapy. The advent of on-board x-ray imaging device mounted on the medical linear accelerator (LINAC) has provided a tool to obtain valuable anatomic information of the patient in the treatment position. However, due to the slow rotating nature of the on-board imaging system (~1 min per rotation), obtaining volumetric information in real time is extremely challenging. Existing methods have relied on grouping many projections acquired over multiple breathing cycles for several minutes to reconstruct one static anatomy. Further, due to the fact that lung cancer patients tend to breathe irregularly, the reconstructed images are often heavily contaminated by breathing motion artifacts. The goal of this research project is to develop innovative real-time volumetric imaging methods that are able to reconstruct the dynamic patient anatomy in real time (~0.1 s) using a single x-ray projection during dose delivery. This bold goal is made practical by three integral components: effective use of an accurate patient-specific lung motion model, advanced compressed sensing techniques for image reconstruction, and a massively parallel and yet affordable computing platform based on graphics processing units (GPU). During the mentored K99 phase, the candidate will draw on his signal processing and statistical modeling expertise to improve and optimize the patient-specific lung motion model while gaining knowledge in lung patient anatomy and pathology, and to quantitatively evaluate the lung motion model and interpret the clinical significance of the results. During the independent R00 phase, a real-time volumetric imaging method which captures both interfraction anatomical changes and intrafraction breathing motion, will be developed, implemented, and evaluated through systematic phantom and patient studies. Successful completion of this project will overcome a critical barrier to the urgently needed real-time volumetric image guidance in lung cancer radiotherapy and afford a powerful way for us to safely escalate the radiation dose and improve local control of lung cancer. This project fits perfectly with the candidate’s long-term career goal of establishing a high-quality independent research program to develop state-of-the-art x-ray imaging techniques, which will provide real-time image guidance for cancer radiotherapy and ultimately improve the therapeutic ratio and enhance the quality of life for cancer patients. Career development and research training will be an integral component during the mentored phase of this project. This training will be further supplemented with formal coursework at Stanford University School of Medicine, as well as participation in research seminars and scientific meetings. The training and research contributions supported by this K99/R00 award will substantially enhance the candidate’s career and serve to establish him as a successful independent investigator in the near future.

分流的解剖变化和施法内呼吸运动是升级的主要限制因素 辐射剂量并改善肺癌放射疗法的局部控制。板载X射线成像的冒险 安装在医疗线性加速器（LINAC）上的设备提供了一种工具来获得有价值的解剖学 患者在治疗位置的信息。但是，由于车载的旋转性质缓慢 成像系统（每次旋转约1分钟），实时获得体积信息是极其挑战的。 现有的方法已救出了为几个通过多个呼吸周期收购的许多项目的分组 分钟重建一种静态解剖结构。此外，由于肺癌患者倾向于呼吸 不规则地，重建的图像通常受到呼吸运动伪像的严重污染。目标 该研究项目是开发能够重建的创新实时体积成像方法 在剂量递送过程中，使用单个X射线投影实时使用动态患者解剖结构。这个大胆 目标是通过三个积分组成部分实用的：有效利用精确的患者特异性肺运动 模型，高级压缩传感技术用于图像重建，并且是一个大规模平行的 基于图形处理单元（GPU）的负担得起的计算平台。在指导的K99阶段， 候选人将利用他的信号处理和统计建模专业知识，以改善和优化 患者特异性的肺运动模型同时获得肺部患者解剖学和病理学知识，并 定量评估肺运动模型并解释结果的临床意义。在 独立R00阶段，一种实时体积成像方法，可捕获两者互动解剖学 将通过系统性开发，实施和评估变化和施法内呼吸运动 幻影和患者研究。成功完成该项目将克服对该项目的关键障碍 迫切需要在肺癌放疗中需要实时体积图像指导，并为一种有力的方法提供 我们可以安全地升级辐射剂量并改善对肺癌的局部控制。这个项目非常适合 候选人的长期职业目标是建立一项高质量的独立研究计划 最先进的X射线成像技术将为癌症放射疗法提供实时图像指导 并最终提高治疗比率并提高癌症患者的生活质量。职业 在该项目的修订阶段，开发和研究培训将是不可或缺的组成部分。 这项培训将进一步补充斯坦福大学医学院的正式课程，作为 以及参与研究下水道和科学会议。培训和研究贡献 在此K99/R00奖的支持下，将大大提高候选人的职业生涯，并有助于建立他 在不久的将来，作为成功的独立调查员。