Optimizing MRI for Radiation Therapy Treatment Planning

MRI在放射肿瘤学中的最佳整合受到缺乏收集 重要的先验知识，可用于采样解剖、生物状态和生理运动 个别病人。而一些通用的图像采集方法利用了非特定的低阶 人类MR信号的结构实现了一些适度的加速，丰富了特定的先验知识， 来自相似患者群体以及特定患者的，尚未被有效地利用 指导最佳治疗计划、定位和监测。 我们假设可以准确地推导出病人的生物学、形态和运动模型。 在先验知识的辅助下，从有限数量的样本中进行分析。这些改进将使我们能够减少扫描时间 形态成像显著(不到常规扫描的10%)，支持高效 用于高阶扩散建模的生物成像和创建分层运动冻结图像体 同时提供呼吸、胃肠道收缩和潜在心脏运动的腹部患者 具有概率密度函数的模型，可用于估计分数内运动对 治疗，并最终选择当地导航员实时监测最 对与运动有关的影响对向危险目标或器官提供剂量的敏感。我们将对此进行调查 提出了一种基于先验知识的压缩感知方法来重构密集采样的假设 稀疏采样的DW衰减曲线；执行以前的主成分分析 扫描的FLAIR、对比度增强的T1加权和扩散加权图像体积支持稀疏 用于解剖成像的k空间和用于扩散成像的b值采样.调查潜在的增益 通过将患者特定的先验与总体派生的主成分相结合来加速成像 关于结构和扩散；模拟呼吸和蠕动运动。最后，我们将开发和实施 基于用于二次采样b值和解剖的建模方法的扫描序列。通过这些方法， 我们期望提供有效的解剖和高阶扩散成像，以及引入方法 自动提取患者的分层运动模型，以用于治疗计划和未来 支持治疗监测。 与PAR 18-484(对于NCI)的相关性：这项调查旨在提高效率和 精准放射治疗对脑胶质瘤、其他颅内靶点的疗效 肝内肿瘤。因为放射治疗是治疗这些疾病的标准医疗选择的一部分 患者，这项研究属于NCI的职权范围。

Optimal integration of MRI in Radiation Oncology is hindered by the lack of methods that harvest the significant prior knowledge available to sample the anatomy, biological status, and physiologic motions of individual patients. While some generic image acquisition methods take advantage of non-specific low rank structure of human MR signals to achieve some modest acceleration, the wealth of specific prior knowledge, from both the population of similar patients as well as the specific patient, has yet to be effectively tapped to guide optimal treatment planning, positioning, and monitoring. We hypothesize that biological, morphological, and motion models of the patient can be accurately derived from a limited number of samples aided by prior knowledge. These advances will allow us to reduce scan times dramatically (to less than 10% of conventional scanning) for morphological imaging, support efficient biological imaging for high order diffusion modeling and create hierarchical motion-frozen image volumes of abdominal patients that simultaneously provide breathing, GI contraction, and potentially cardiac motion models with probability density functions that can be used to estimate the impact of intrafraction motion on treatments and eventually select local navigators for real-time monitoring of specific regions that are most sensitive to motion-related impacts on delivered doses to targets or organs at risk. We will investigate this hypothesis by developing a prior knowledge-based compressed sensing method to reconstruct densely sampled DW attenuation curves from sparsely sampled ones; performing principal component analysis of previously scanned FLAIR, contrast-enhanced T1-weighted and Diffusion-Weighted image volumes to support sparse sampling in k-space for anatomic imaging and in b-values for diffusion imaging; investigating potential gains in acceleration of imaging by combining a patient-specific prior with population-derived principal components of structure and diffusion; modeling breathing and peristaltic motion. Finally, we will develop and implement scanning sequences based on the modeled methods for subsampling b-values and anatomy. By these methods, we expect to provide efficient anatomic and high order diffusion imaging, as well as introduce means to automatically extract hierarchical motion models of the patient for use in treatment planning and future support of treatment monitoring. Relevance to PAR 18-484 (for the NCI): This investigation seeks to improve both the efficiency as well as the efficacy of precision radiation therapy for patients with GBMs, other intracranial targets as well as intrahepatic tumors. As Radiation therapy is part of the standard armamentarium of care options for these patients, this research falls within the purview of the NCI.