Real-Time Volumetric Imaging for Lung Cancer Radiotherapy

肺癌放射治疗的实时体积成像

基本信息

批准号：
8279092
负责人：
Ruijiang Li
金额：
$ 16.06万
依托单位：
STANFORD UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2012
资助国家：
美国
起止时间：
2012-08-02 至 2014-07-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8279092
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 System Techniques Therapeutic Time Toxic effect Training Universities Work X-Ray Computed Tomography base cancer radiation therapy career career development clinically significant computerized data processing digital flexibility image reconstruction imaging modality improved innovation lung volume medical schools meetings parallel computing programs reconstruction research and development respiratory simulation time use tool treatment duration tumor

项目摘要

DESCRIPTION (provided by applicant): 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. PUBLIC HEALTH RELEVANCE: This project aims to develop real-time volumetric imaging methods that are able to reconstruct the real-time dynamic patient anatomy using a single x-ray projection during dose delivery. Successful completion of the project will overcome a critical barrier to the urgently needed real-time volumetric image guidance in lung cancer radiotherapy. It will provide a critically needed means to treat lung cancer and afford a powerful way for us to safely escalate the radiation dose and improve local control of lung cancer.

描述（由申请人提供）：分次间解剖学变化和分次内呼吸运动是增加辐射剂量和改善局部治疗的主要限制因素。肺癌放射治疗的控制。安装在医用直线加速器 (LINAC) 上的机载 X 射线成像设备的出现，为获取处于治疗位置的患者的有价值的解剖信息提供了一种工具。然而，由于机载成像系统旋转缓慢（每次旋转约 1 分钟），实时获取体积信息极具挑战性。现有的方法依赖于对在几分钟的多个呼吸周期中获得的许多投影进行分组来重建一个静态解剖结构。此外，由于肺癌患者往往呼吸不规则，重建图像常常受到呼吸运动伪影的严重污染。该研究项目的目标是开发创新的实时体积成像方法，能够在剂量输送期间使用单个 X 射线投影实时（~0.1 秒）重建动态患者解剖结构。这一大胆的目标通过三个组成部分得以实现：有效利用准确的患者特定肺运动模型、用于图像重建的先进压缩传感技术，以及基于图形处理单元 (GPU) 的大规模并行且经济实惠的计算平台。在 K99 指导阶段，候选人将利用其信号处理和统计建模专业知识来改进和优化患者特定的肺运动模型，同时获得肺患者解剖学和病理学方面的知识，并定量评估肺运动模型并解释肺运动模型。结果的临床意义。在独立 R00 阶段，将通过系统体模和患者研究来开发、实施和评估实时体积成像方法，该方法可捕获分次间解剖变化和分次内呼吸运动。该项目的成功完成将克服肺癌放疗中迫切需要的实时体积图像引导的关键障碍，并为我们安全地增加辐射剂量和改善肺癌的局部控制提供有力的方法。该项目完全符合候选人的长期职业目标，即建立高质量的独立研究项目，开发最先进的 X 射线成像技术，为癌症放射治疗提供实时图像指导，并最终改善癌症放射治疗的效果。治疗率并提高癌症患者的生活质量。职业发展和研究培训将是该项目指导阶段不可或缺的组成部分。该培训将得到斯坦福大学医学院的正式课程以及参加研究研讨会和科学会议的进一步补充。 K99/R00 奖项支持的培训和研究贡献将极大地提高候选人的职业生涯，并有助于使他在不久的将来成为一名成功的独立研究者。公共健康相关性：该项目旨在开发实时体积成像方法，能够在剂量输送期间使用单个 X 射线投影重建实时动态患者解剖结构。该项目的成功完成将克服肺癌放射治疗中迫切需要的实时体积图像引导的关键障碍。它将提供治疗肺癌急需的手段，并为我们安全增加辐射剂量和改善肺癌的局部控制提供有力的方法。