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）：呼吸运动导致肺癌放射治疗（RT）中显着的几何和剂量不确定性。这种不确定性的影响在立体定向全身放射治疗 (SBRT) 等大分割治疗方案中被放大，其中以相对较少的分割次数提供非常高、有效的剂量。肺部 SBRT 实现了出色的局部控制 (>80%)，但也显示出显着的附带毒性 (10 - 28%)。一些临床研究报告了毒性和辐射剂量之间的密切相关性。胸部解剖结构在所有四个维度（4D = 3D + 时间）中随着周期和周期的变化而不断变化。当前运动管理技术的一个常见限制是它们丢弃了大量的 4D 信息，并且不能捕获或充分考虑周期间的变化。我们假设在每个 RT 步骤中完全考虑所有四个维度将显着改善剂量节省，从而降低毒性。为了响应 PAR-10-169，我们在 UT 西南医学中心 (UTSW)、犹他大学 (犹他州)、马里兰大学 (UMD)、瓦里安医疗系统和 VisionRT 之间建立了多学科学术-工业合作关系。我们的目标是创建一个全面的 4DRT 运动管理解决方案，与当前临床肺 SBRT 相比，可实现串行结构 50% 的剂量节省，以及正常肺的 30-50% 的剂量节省。为了实现这一目标，我们提出了一个系统的、以假设为驱动的研究计划。在目标 1 中，我们将研究一种新型的无分箱最大后验 (MAP) 4DCT 重建。 4DCT 将通过实时表面摄影测量 (VisionRT) 进行参数化，以创建高时空分辨率的 4D 运动模型，该模型将内部体积描述为多个呼吸周期内外表面的函数。 VisionRT 系统安装在 CT 模拟室和治疗室中，从而充当 CT 模拟和剂量输送阶段之间的公共链接。在目标 2 中，我们将研究 4D 优化，以创建可交付的治疗计划，该计划考虑了多个呼吸周期的运动。我们还将研究使用运动作为附加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) 跟踪的实时光束自适应。该技术将重塑光束，以便跟踪肿瘤和周围器官的所有复杂变化（平移、旋转和变形）。我们将通过每个交付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纳入两个研究 4DRT 原型中，这些原型将部署在 UTSW 和 UMD 进行最终用户验证。将使用可变形肺运动模型和肺癌患者的数据进行验证。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来制定实践指南、质量保证和教育框架，以促进临床转化。