Personalized Motion Management for truly 4D Lung Stereotactic Body Radiotherapy

个性化运动管理，实现真正的 4D 肺部立体定向放射治疗

• 批准号: 9233633
• 负责人: Amit Sawant
• 金额: $ 52.85万
• 依托单位: UNIVERSITY OF MARYLAND BALTIMORE
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-09-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9233633
• 关键词: Personalized; Motion; Management; truly; 4D

DESCRIPTION (provided by applicant): Respiratory motion causes significant geometric and dosimetric uncertainties in lung cancer radiotherapy (RT). The impact of such uncertainties is amplified in hypofractionated regimens such as stereotactic body radiotherapy (SBRT), where very high, potent doses are delivered in relatively few fractions. Lung SBRT achieves excellent local control (>80%) but also shows significant collateral toxicity (10 - 28%). Several clinical studies have reported a strong correlation between toxicity and radiation dose. Thoracic anatomy changes continuously in all four dimensions (4D=3D+time) from cycle-to-cycle and day-to- day. A common limitation of current motion management techniques is that they discard large amounts of this 4D information and do not capture nor adequately account for cycle-to-cycle variations. We hypothesize that completely accounting for all four dimensions at each RT step will significantly improve dose-sparing and, consequently, lead to reduced toxicity. In response to PAR-10-169, we form a multidisciplinary academic-industrial collaboration between UT Southwestern Medical Center (UTSW), University of Utah (Utah), University of Maryland (UMD), Varian Medical Systems and VisionRT. Our goal is to create a comprehensive 4DRT motion management solution that achieves e50% dose-sparing of serial structures and 30-50% more sparing of normal lung compared to current clinical lung SBRT. Towards this goal, we present a systematic, hypothesis-driven research plan. In Aim 1, we will investigate a novel binning-free maximum a posteriori (MAP) 4DCT reconstruction. The 4DCT will be parameterized by real-time surface photogrammetry (VisionRT) to create a high-spatiotemporal- resolution 4D motion model that describes the internal volume as a function of external surface over several respiratory cycles. The VisionRT system is installed in the CT-simulation room as well as the treatment room, thus serving as a common link between the CT-sim and the dose delivery stages. In Aim 2, we will investigate 4D optimization to create deliverable treatment plans that account for motion over multiple respiratory cycles. We will also investigate the novel concept of using motion as an additional degree of freedom rather than a constraint. In Aim 3, we will investigate real-time beam adaptation using multileaf collimator (MLC) tracking. This technique will reshape the beam so as to follow all of the complex changes (translation, rotation and deformation) of the tumor and surrounding organs. We will investigate closed-loop RT via a voxel-level dosimetric reconstruction of each delivered fraction; to be used for verification and, f necessary, for daily replanning. Our industrial partners will incorporate our research findings int two research 4DRT prototypes which will be deployed at UTSW and UMD for end-user validation. Validation will be performed using a deformable lung motion phantom and data from lung cancer patients. The latter will consist of 4DCT, and surface tracking data and in-room kV x-ray fluoroscopy. Finally, we will form physician-physicist teams to develop practice guidelines, quality assurance and education frameworks to facilitate clinical translation.

描述（由申请人提供）：呼吸运动在肺癌放射治疗（RT）中引起显著的几何和剂量学不确定性。这种不确定性的影响在立体定向体放射治疗（SBRT）等低分割治疗方案中被放大，在这种治疗方案中，非常高的强效剂量以相对较少的部分提供。肺SBRT获得了良好的局部控制（>80%），但也显示出明显的附带毒性（10 - 28%）。一些临床研究报告毒性与辐射剂量之间存在很强的相关性。每一个周期和每一天，胸部解剖结构在所有四个维度（4D=3D+时间）上都在不断变化。当前运动管理技术的一个共同限制是，它们丢弃了大量的4D信息，并且没有捕获也没有充分考虑周期到周期的变化。我们假设在每个RT步骤中完全考虑所有四个维度将显著改善剂量节约，从而导致毒性降低。为了响应PAR-10-169，我们与UT西南医学中心（UTSW）、犹他大学（Utah）、马里兰大学（UMD）、瓦里安医疗系统和VisionRT建立了多学科的学术-工业合作。我们的目标是创建一个全面的4DRT运动管理解决方案，与目前的临床肺SBRT相比，该解决方案可实现系列结构的剂量节约50%，正常肺的剂量节约30-50%。为了实现这一目标，我们提出了一个系统的，假设驱动的研究计划。在目标1中，我们将研究一种新的无分帧的最大后验（MAP） 4DCT重建。4DCT将通过实时表面摄影测量（VisionRT）进行参数化，以创建一个高时空分辨率的4D运动模型，该模型将内部体积描述为几个呼吸周期内外部表面的函数。VisionRT系统安装在ct模拟室和治疗室中，从而作为ct模拟和给药阶段之间的共同链接。在目标2中，我们将研究4D优化，以创建可交付的治疗计划，考虑多个呼吸周期的运动。我们还将研究使用运动作为额外自由度而不是约束的新概念。在目标3中，我们将研究使用多叶准直器（MLC）跟踪的实时光束适应。这种技术将重塑光束，以便跟随肿瘤和周围器官的所有复杂变化（平移、旋转和变形）。我们将通过对每个递送部分的体素级剂量重建来研究闭环RT；用于验证，必要时用于日常重新规划。我们的工业合作伙伴将把我们的研究成果整合到两个研究4DRT原型中，这些原型将在UTSW和UMD部署，以供最终用户验证。验证将使用可变形的肺运动假体和肺癌患者的数据进行。后者将包括4DCT、表面跟踪数据和室内kV x射线透视。最后，我们将组建医师-物理学家团队，制定实践指南、质量保证和教育框架，以促进临床翻译。