Real-Time Volumetric Imaging for Lung Cancer Radiotherapy

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 肺癌放疗中的对照。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提供了一种工具，以获得患者在治疗位置的有价值的解剖信息。然而，由于机载成像系统的缓慢旋转性质（每次旋转约1分钟），真实的时间内获得体积信息极具挑战性。现有的方法依赖于对在多个呼吸周期内采集的多个投影进行分组几分钟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，重建的图像往往受到呼吸运动伪影的严重污染。本研究项目的目标是开发创新的实时容积成像方法，能够在剂量输送期间使用单个X射线投影在真实的时间（~0.1 s）内重建动态患者解剖结构。这一大胆的目标通过三个不可或缺的组成部分实现：有效使用准确的患者特定肺部运动模型，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。在辅导K99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部运动模型，同时获得肺部患者解剖学和病理学方面的知识，并定量评估肺部运动模型并解释结果的临床意义。在独立R 00阶段，将通过系统体模和患者研究开发、实施和评价实时容积成像方法，该方法可捕获分次间解剖结构变化和分次内呼吸运动。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，并为我们安全地提高辐射剂量和改善肺癌的局部控制提供强有力的方法。该项目完全符合候选人的长期职业目标，即建立一个高质量的独立研究计划，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，最终提高治疗率，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辅导阶段的一个组成部分。这种培训将得到斯坦福大学医学院正式课程的进一步补充，并参加研究研讨会和科学会议。K99/R 00奖项所支持的培训和研究贡献将大大提高候选人的职业生涯，并有助于在不久的将来将他确立为一名成功的独立调查员。

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.