Development of Anatomical Patient Models to Facilitate MR-only Treatment Planning

开发患者解剖模型以促进纯 MR 治疗计划

• 批准号: 9306036
• 负责人: Carri Kaye Glide-Hurst
• 金额: $ 30.48万
• 依托单位: HENRY FORD HEALTH SYSTEM
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-07-01 至 2021-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9306036
• 关键词: 3D Print; Academia; Address; Anatomic Models; Anatomy; Area; Atlases; Benchmarking; Biological; Brain; Clinic; Clinical; Collaborations; Community Clinical Oncology Program; Consensus; Data; Data Set; Development; Disease; Dose; Ensure; Evaluation; Female; Functional Magnetic Resonance Imaging; Future; Generations; Goals; Health system; Healthcare; Hybrids; Image; Implant; Industrialization; Industry; Magnetic Resonance Imaging; Malignant Neoplasms; Malignant neoplasm of brain; Malignant neoplasm of cervix uteri; Malignant neoplasm of prostate; Measures; Metals; Modeling; Multi-Institutional Clinical Trial; Normal tissue morphology; Organ; Orthopedics; Patient-Focused Outcomes; Patients; Pelvic Cancer; Pelvis; Phase; Physicians; Play; Practice Guidelines; Process; Radiation; Radiation Oncology; Radiation therapy; Research; Research Personnel; Residual state; Resolution; Risk; Role; Scanning; Site; Techniques; Technology; Testing; Time; Toxic effect; Translating; Translations; Tumor Burden; Uncertainty; Validation; Vendor; Work; X-Ray Computed Tomography; base; bone; clinical practice; correctional system; cost; density; design; digital; dosimetry; electron density; image guided; image guided radiation therapy; image processing; image reconstruction; image registration; imaging Segmentation; imaging platform; imaging system; improved; innovation; innovative technologies; interest; male; multidisciplinary; novel; patient population; population based; radiation risk; simulation; soft tissue; standard of care; tool; treatment planning; tumor; virtual; virtual clinical trial

Accurate delineation of targets and organs at risk for radiation therapy planning (RTP) remains a challenge due to the lack of soft tissue contrast in computed tomography (CT), the standard of care imaging for RTP. Radiation Oncology has addressed this limitation by registering magnetic resonance images (MRI) to CT datasets to take advantage of the superior soft tissue contrast afforded by MRI. MRI brings considerable value to RTP by improving delineation accuracy which, in turn, has enabled dose escalation to improve local control while maintaining or reducing normal tissue toxicities. However, the current integration of MRI as an adjunct to CT has significant drawbacks as it requires image registration and contour transfer between datasets. This process introduces systematic geometric uncertainties that persist throughout treatment and may compromise tumor control. Thus, we propose to translate MR-only RTP into clinical use, with the ultimate goal of improving patient outcomes accomplished via improved treatment plan design. MR-only RTP will eliminate redundant CT scans (reducing dose, patient time, and costs), streamline clinical efficiency, entirely circumvent registration uncertainties, and fully exploit the benefits of MRI for high-precision RTP. Yet, MRI is not routinely used alone for RTP, largely due to its known spatial distortions, lack of electron density, and inability to segment the bone needed for online image guidance and electron density mapping for dose calculation. The central hypothesis is that the innovative technologies that our multi-disciplinary academic/industrial (Henry Ford Health System/Philips Healthcare) collaboration develop will yield geometrically accurate patient models built from MRI data across several platforms/field strengths with CT-equivalent densities that can be used in confidence throughout the entire RTP workflow. In Aim 1, we will perform geometric distortion corrections, determine distortion variability with changing anatomy, benchmark the results in a novel modular phantom, and develop an image processing toolkit. In Aim 2, we will fully automate MR image segmentation in the brain and male/female pelvis to yield accurate synthetic CT patient models derived from novel MRI sequences, including provisions for metal implants, and benchmark the results in phantom. In Aim 3, we will conduct end-to-end testing to characterize the uncertainties in the MR-only RTP workflow. We will perform a virtual clinical trial of MR-only RTP for brain and male/female pelvis and compare to the standard of care. Final translation will include developing physician-physicist practice guidelines, end-user validation of all translational steps, and dissemination of image processing tools into the Radiation Oncology community. This research will systematically address the major challenges limiting MR-only RTP and lay the groundwork for multi-institutional clinical trials across MRI platforms. It will support future work related to MR-guided RT, functional MRI for biologically adaptive RT, and focal RT to areas of high tumor burden.

准确描绘有放射治疗计划风险的目标和器官（RTP）仍然是一个挑战 由于缺乏计算机断层扫描（CT）的软组织对比度，这是RTP的护理成像标准。 辐射肿瘤学通过将磁共振图像（MRI）注册到CT来解决此限制 数据集利用MRI提供的优质软组织对比。 MRI带来了相当大的价值 通过提高描述准确性来提高RTP，这反过来又使剂量升级以改善本地控制 同时维持或减少正常的组织毒性。但是，MRI当前集成为 CT具有重要的缺点，因为它需要图像注册和数据集之间的轮廓传输。这 过程引入了系统的几何不确定性，这些不确定性在整个治疗过程中持续存在并可能妥协 肿瘤控制。因此，我们建议将仅MR的RTP转化为临床使用，最终目的是改善 通过改进的治疗计划设计实现的患者结果。 MR仅RTP将消除冗余CT 扫描（减少剂量，患者时间和成本），简化临床效率，完全规避注册 不确定性，并充分利用MRI对高精度RTP的好处。但是，MRI并不是通常单独使用的 对于RTP，很大程度上是由于其已知的空间变形，缺乏电子密度和无法分割骨头 在线图像指导和电子密度映射所需的剂量计算所需。 中心假设是我们多学科学术/工业的创新技术 （Henry Ford Health System/Philips Healthcare）协作发展将产生几何准确的患者 由MRI数据构建的模型，跨多个平台/现场优势，具有CT等效密度 在整个RTP工作流程中使用信心。在AIM 1中，我们将执行几何失真 校正，通过改变解剖结构来确定失真变异性，基准在新型模块化中基准结果 幻影，并开发图像处理工具包。在AIM 2中，我们将完全自动化MR图像分段 大脑和雄性/雌性骨盆得出来自新型MRI的准确合成CT患者模型 序列，包括金属植入物的规定，并将结果基准为幻影。在AIM 3中，我们将 进行端到端测试以表征仅MR RTP工作流程中的不确定性。我们将执行 仅MR的RTP用于脑和雄性骨盆的虚拟临床试验，并与护理标准进行比较。最终的 翻译将包括制定医师 - 物理学实践指南，最终用户对所有翻译的验证 步骤，以及将图像处理工具传播到辐射肿瘤学界。这项研究会 系统地应对限制MR仅RTP的主要挑战，并为多机构的基础奠定基础 MRI平台进行临床试验。它将支持与MR引导RT，功能性MRI相关的未来工作 具有生物自适应的RT，并焦点为高肿瘤负担的区域。