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

放射治疗计划（RTP）中靶点和危险器官的准确描绘仍然是一个挑战 由于RTP的护理成像标准--计算机断层扫描（CT）中缺乏软组织对比度。 放射肿瘤学通过将磁共振图像（MRI）配准到CT来解决这一限制 利用MRI提供的上级软组织对比度。MRI带来了相当大的价值 通过提高描绘准确性，从而使剂量递增能够改善局部控制， 同时维持或降低正常组织毒性。然而，目前MRI作为辅助手段的整合， CT具有显著的缺点，因为它需要图像配准和数据集之间的轮廓转移。这 过程引入了系统性几何不确定性，在整个治疗过程中持续存在， 肿瘤控制因此，我们建议将仅MR RTP转化为临床应用，最终目标是改善 通过改进的治疗计划设计实现患者结局。仅MR 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，完全避免注册 充分利用MRI的优势，实现高精度RTP。然而，MRI并不是常规单独使用的 对于RTP，主要是由于其已知的空间失真，缺乏电子密度，以及无法分割骨 需要在线图像引导和电子密度绘图来进行剂量计算。 核心假设是，我们的多学科学术/工业 （亨利福特健康系统/飞利浦医疗保健）合作开发将产生几何精确的患者 根据多个平台/场强的MRI数据构建的模型，具有CT等效密度， 在整个RTP工作流程中保密使用。在目标1中，我们将执行几何失真 校正，确定随着解剖结构变化的失真可变性，在一种新型的模块化 幻影，并开发一个图像处理工具包。在目标2中，我们将完全自动化MR图像分割， 大脑和男性/女性骨盆产生精确的合成CT患者模型， 序列，包括金属植入物的规定，并在体模中对结果进行基准测试。在目标3中，我们 进行端到端测试，以表征仅MR RTP工作流程中的不确定性。我们将执行一个 仅MR RTP用于脑部和男性/女性骨盆的虚拟临床试验，并与标准治疗进行比较。最终 翻译将包括制定医生-物理学家实践指南，最终用户验证所有翻译 步骤，并将图像处理工具传播到放射肿瘤学社区。这项研究将 系统地解决限制仅限MR RTP的主要挑战，并为多机构 MRI平台的临床试验。它将支持未来与MR引导RT、功能MRI相关的工作， 生物适应性RT和高肿瘤负荷区域的局灶性RT。

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.