Personalized Motion Management for Truly 4D Lung Radiotherapy

真正 4D 肺部放射治疗的个性化运动管理

基本信息

批准号：
10274050
负责人：
RAO P GULLAPALLI
金额：
$ 65.85万
依托单位：
UNIVERSITY OF MARYLAND BALTIMORE
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-07-16 至 2026-06-30
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10274050
关键词：
3-Dimensional 4D MRI Address Adopted Adoption Aftercare Anatomy Breathing Cancer Patient Chest Clinical Clinical Research Collaborations Complex Data Dose Electromagnetics Engineering Evaluation Failure Fluoroscopy Four-dimensional Guidelines Imaging Techniques Industrialization Institution Lead Location Lung Magnetic Resonance Imaging Malignant neoplasm of lung Maryland Medical Modeling Monitor Motion Optics Organ Patients Performance Photogrammetry Positioning Attribute Prospective Studies Quality of life Radiation Radiation therapy Research Research Design Respiration Risk Robin bird Shapes Structure of parenchyma of lung Surface System Techniques Testing Time Toxic effect Update Variant X-Ray Computed Tomography base clinical translation cost electron density experience image guided image guided radiation therapy industry partner interest novel pre-clinical prospective prototype quality assurance radiation delivery real time monitoring respiratory response sensor side effect simulation soft tissue spatiotemporal standard of care tumor

项目摘要

It is well-recognized that unanticipated respiration-induced motion can result in significant errors in planned vs delivered dose in thoracic radiotherapy (RT), resulting in local regional failure and/or increased radiation-induced toxicity. In this proposal, we build upon our previous motion management research and aim to overcome the limitations of current motion management strategies, which tend to underrepresent both the extent and the spatiotemporal complexity of respiratory motion. Our overall premise is that, as our field adopts increasingly more potent forms of RT, real-time single-point monitoring needs to be replaced by real-time volumetric monitoring to capture complex motion. Recently available integrated magnetic resonance imaging (MRI)+Linac systems aim to address the limitations of current conventional solutions. However, the high cost and complexity of these systems, as well as engineering and technological challenges, have proven to be substantial barriers to their widespread clinical adoption (less than 1% of the total US install base for linacs). To address this unmet clinical need, we form an academic-industrial partnership to investigate and develop a novel in-room real-time motion management solution for lung RT that combines 4DMRI and 4DCT (4D=3D+time). In Aim 1, we develop and investigate rapid 4DMRI techniques. In Aim 2, we merge the volumetric motion information derived from 4DMRI and 4DCT to create a patient-specific, multi-cycle motion model that incorporates the geometric fidelity and electron density information from CT with the soft-tissue contrast and dose-free, long-term monitoring from MRI. This model is parameterized by the spatial positions of MRI-compatible electromagnetic (EM) sensors placed on the thoracoabdominal surface of the patient. By knowing the position of these sensors at any given time point, we can estimate the corresponding position of each voxel within the irradiated volume. At each treatment fraction, the model is rebuilt using in-room kV fluoroscopy prior to delivery to account for inter-fraction (day-to-day) changes in external-internal correspondence and updated using kV fluoro during dose delivery to account for intra-fraction changes. In Aim 3, we develop two identical preclinical prototype systems (EndoScoutRT) and form end-user teams tasked with formulating clinical workflows, quality assurance guidelines, and strategies for clinical translation. In Aim 4, we perform end-user evaluation of the prototype systems by conducting a prospective non-interventional clinical study in 44 lung cancer patients at two institutions. We compare the performance of our model-based motion management to current standard-of-care and MRI+Linac based real-time motion management. Our team has extensive expertise in clinical study design, image-guided RT, rapid MRI, and real-time motion management. We anticipate that the successful clinical translation of this approach (beyond the current scope) will enable safer administration of highly potent and clinically effective forms of thoracic RT.

众所周知，意外的呼吸引起的运动可能会导致严重的错误胸部放射治疗 (RT) 中的计划剂量与交付剂量，导致局部区域失败和/或增加辐射引起的毒性。在这个提案中，我们建立在我们之前的运动管理研究和目标的基础上克服当前运动管理策略的局限性，这些策略往往不足以代表呼吸运动的程度和时空复杂性。我们的总体前提是，随着我们的领域采用 RT 形式越来越有效，实时单点监控需要被实时取代体积监测以捕捉复杂的运动。最近推出的集成磁共振成像 (MRI)+直线加速器系统旨在解决当前传统解决方案的局限性。但成本较高这些系统的复杂性以及工程和技术挑战已被证明是其广泛临床应用面临巨大障碍（不到美国直线加速器安装总数的 1%）。为了解决这一未满足的临床需求，我们建立了学术-工业合作伙伴关系来调查和开发一种结合 4DMRI 和 4DCT 的新型室内实时运动管理解决方案，用于肺部 RT （4D=3D+时间）。在目标 1 中，我们开发并研究快速 4DMRI 技术。在目标 2 中，我们合并从 4DMRI 和 4DCT 导出的体积运动信息可创建患者特定的多周期运动将来自 CT 的几何保真度和电子密度信息与软组织相结合的模型通过 MRI 进行对比和无剂量长期监测。该模型由空间位置参数化与 MRI 兼容的电磁 (EM) 传感器放置在患者的胸腹部表面。经过知道这些传感器在任何给定时间点的位置，我们就可以估计出相应的位置照射体积内的每个体素。在每个处理部分，使用室内 kV 重建模型分娩前进行透视检查，以考虑外部内部的分数间（每日）变化对应并在剂量输送期间使用 kV 荧光进行更新，以考虑分次内的变化。瞄准 3，我们开发两个相同的临床前原型系统（EndoScoutRT）并组建最终用户团队，负责制定临床工作流程、质量保证指南和临床转化策略。在目标 4 中，我们通过进行前瞻性非干预性临床，对原型系统进行最终用户评估对两家机构的 44 名肺癌患者进行的研究。我们比较基于模型的运动的性能管理到当前的护理标准和基于 MRI+Linac 的实时运动管理。我们的团队有在临床研究设计、图像引导 RT、快速 MRI 和实时运动管理方面拥有丰富的专业知识。我们预计这种方法的成功临床转化（超出当前范围）将使更安全地实施高效且临床有效的胸部放疗形式。