Optimizing MRI for Radiation Therapy Treatment Planning

优化 MRI 以制定放射治疗计划

• 批准号: 9979862
• 负责人: JAMES M BALTER
• 金额: $ 34.49万
• 依托单位: UNIVERSITY OF MICHIGAN AT ANN ARBOR
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-04-01 至 2022-11-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9979862
• 关键词: Abdomen; Acceleration; Anatomy; Area; Base Sequence; Biological; Biological Monitoring; Biology; Breathing; Caring; Community Clinical Oncology Program; Complex; Data; Databases; Development; Diffusion; Diffusion Magnetic Resonance Imaging; Dose; Freezing; Future; Glioblastoma; Harvest; Head; Human; Image; Intestines; Investigation; Knowledge; Magnetic Resonance; Magnetic Resonance Imaging; Maps; Methodology; Methods; Modeling; Monitor; Morphologic artifacts; Morphology; Motion; Movement; Normal tissue morphology; Organ; Patients; Pattern; Periodicity; Peristalsis; Physiological; Population; Positioning Attribute; Principal Component Analysis; Probability; Radiation Dose Unit; Radiation Oncology; Radiation therapy; Research; Risk; Sampling; Scanning; Selection for Treatments; Shapes; Signal Transduction; Speed; Structure; System; Techniques; Technology; Testing; Time; Tissues; Tumor Tissue; anatomic imaging; attenuation; base; cancer imaging; contrast enhanced; cost; density; falls; gastrointestinal; heart motion; improved; individual patient; intrahepatic cancer; knowledge base; optimal treatments; patient population; prospective; real time monitoring; reconstruction; response; shape analysis; simulation; theories; tool; treatment planning; tumor

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.

MRI在放射肿瘤学中的最佳整合受到缺乏获取放射治疗的方法的阻碍。 重要的先验知识，可用于采样的解剖结构，生物状态，和生理运动， 个别患者。虽然一些通用的图像采集方法利用非特异性低秩 结构的人类MR信号，以实现一些适度的加速，丰富的具体先验知识， 从类似患者的群体以及特定患者中，尚未有效地挖掘， 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患者的生物学、形态学和运动模型 从有限数量的样本中，借助先验知识。这些进步将使我们能够减少扫描时间 显着（不到10%的传统扫描）的形态成像，支持高效 用于高阶扩散建模的生物成像，并创建 腹部患者同时提供呼吸、GI收缩和潜在的心脏运动 具有概率密度函数的模型，可用于估计分数内运动对 治疗，并最终选择本地导航实时监控的特定区域， 对运动相关的影响敏感，对目标或危险器官的输送剂量敏感。我们会调查的 假设通过开发基于先验知识的压缩感知方法来重建密集采样 从稀疏采样的DW衰减曲线;执行主成分分析先前 扫描FLAIR、对比增强T1加权和弥散加权图像体积，以支持稀疏 解剖成像在k空间中采样，扩散成像在b值中采样;研究潜在增益 在通过将患者特异性先验与群体导出的主成分相结合来加速成像中 结构和扩散;模拟呼吸和蠕动运动。最后，我们将制定和实施 基于用于二次采样b值和解剖结构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学和高阶扩散成像，以及引入手段， 自动提取患者的分层运动模型以用于治疗计划和将来的治疗计划。 支持治疗监测。 与PAR 18-484（NCI）的相关性：本研究旨在提高效率， 精确放射治疗对GBM患者、其他颅内靶点以及 肝内肿瘤由于放射治疗是这些患者的标准医疗保健选择的一部分， 患者，这项研究福尔斯NCI的范围。