TRD 3: MRI parameters reflecting tissue composition and microstructure

TRD 3：反映组织成分和微观结构的 MRI 参数

• 批准号: 10439905
• 负责人: Peter CM Van Zijl
• 金额: $ 19.44万
• 依托单位: HUGO W. MOSER RES INST KENNEDY KRIEGER
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-07-01 至 2026-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10439905
• 关键词: Affect; Anatomy; Anisotropy; Atlases; Biological Markers; Biological Process; Blood; Brain; Calcium; Characteristics; Data; Development; Diagnostic; Diffusion; Diffusion Magnetic Resonance Imaging; Disease; Ferritin; Fiber; Fingerprint; Frequencies; Goals; Head; Hemoglobin; Image; Imaging Device; Iron; Label; Lead; Libraries; Lipids; Magnetic Resonance Imaging; Magnetism; Measurement; Measures; Metabolic; Methods; Microscopic; Molecular; Motion; Myelin; Organ; Pathogenicity; Permeability; Physiologic pulse; Physiological; Predisposition; Principal Investigator; Process; Property; Proteins; Protons; Radiology Specialty; Reading; Relaxation; Reporting; Research Personnel; Resolution; Resources; Rotation; Sampling; Sequence Analysis; Shapes; Signal Transduction; Structure; Technology; Testing; Therapeutic Intervention; Time; Tissues; Universities; Variant; Water; age related; base; brain tract; candidate marker; contrast imaging; deep learning; density; design; gray matter; high resolution imaging; imaging biomarker; imaging study; in vivo; in vivo imaging; learning strategy; magnetic field; potential biomarker; professor; quantitative imaging; radiologist; reconstruction; response; statistical learning; synergism; tractography; water diffusion; white matter

TRD3: MRI parameters reflecting tissue composition and microstructure Lead Principal investigator: Peter van Zijl, Professor of Radiology Co-investigators: Xu Li, Manisha Aggarwal, Hye-Young Heo, Jeremias Sulam, Susumu Mori Consultant: Filip Szczepankiewicz (Lund University) While TRDs 1 and 2 focus on MR approaches that measure actual physiological constants and metabolite signals, the definition of a Quantitative Imaging Biomarker (QIB) goes much further. In TRD3 we therefore exploit the inherent power of MRI to probe tissue composition and microstructure, the characteristics of which can be accessed through a multitude of MRI phenomena and parameters that can be seen as candidate biomarkers. The intensity and frequency of the water signal in an MRI voxel depend on the local microscopic fields and field differences imposed by tissue compartments and molecules. In addition, the motion of water measured by MRI is affected by compartment size and permeability, which may change in disease and thus contain potential biomarker information. The overall goal of this TRD is to design pulse sequences and analysis approaches to efficiently quantify MRI parameters that assess tissue composition and microstructure. We have the following specific aims: AIM 1: Development of compartmental filtering and diffusional encoding methods to probe tissue microstructure. AIM 2: Development of integrated susceptibility and diffusion tensor imaging (STI and DTI) for fiber tractography, aiming at high resolution white matter fiber tractography in vivo. Gray matter iron content and blood oxygenation will also be assessed from these high-resolution susceptibility images AIM 3: Development of fast multi-parameter acquisition and analysis approaches for simultaneous quantification of the MR-derived parameters in Aims 1 and 2 plus T1, T2(*), and Magnetization Transfer Ratio (MTR). The parameters obtained will be used to synthetically generate multiple image contrasts (synthetic MRI), including conventional ones with which the radiologists are familiar for reading and that currently can be acquired only separately. Eight CPs will be involved in optimizing the methods and testing these approaches for biomarker potential. Eight SPs will use them to extend the information content in their studies. The developed tissue markers together with the diagnostic parameters of TRD1 and TRD2 will be made available to TRD4, which will develop statistical and deep learning technologies to combine them and make them available in age-dependent multi-parameter brain atlases.

TRD3：反映组织组成和微观结构的MRI参数 首席研究员：放射学教授Peter Van Zijl 共同投资者：徐李，曼尼沙·阿格加尔瓦尔，Hye-Young Heo，Jeremias Sulam，Susumu Mori 顾问：Filip Szczepankiewicz（隆德大学） 而TRDS 1和2专注于测量实际生理常数和代谢物的MR方法 信号，定量成像生物标志物（QIB）的定义进一步。因此，在TRD3中我们 利用MRI对探测组织组成和微结构的固有力， 可以通过多种MRI现象和参数来访问这些方法 候选生物标志物。 MRI体素中水信号的强度和频率取决于 组织区室和分子施加的局部微观场和场差。此外， 通过MRI测量的水的运动受隔室尺寸和渗透率的影响，这可能 疾病的变化，因此包含潜在的生物标志物信息。这个TRD的总体目标是 设计脉冲序列和分析方法有效量化评估的MRI参数 组织组成和微观结构。我们有以下具体目标： 目标1：开发隔室滤波和扩散编码方法探测组织 微观结构。 AIM 2：纤维的综合易感性和扩散张量成像（STI和DTI） 拖拉术，针对体内的高分辨率白质纤维拖拉术。灰质铁含量 还将从这些高分辨率易感图像中评估血液氧合 目标3：同时开发快速多参数获取和分析方法 在目标1和2加T1，T2（*）和磁化转移中对MR衍生参数的定量 比率（MTR）。 获得的参数将用于合成生成多个图像对比度（合成MRI）， 包括传统的放射科医生熟悉阅读的传统，目前可以是 仅分别获得。八个CP将参与优化方法并测试这些方法 生物标志物潜力。八个SP将使用它们扩展其研究中的信息内容。这 将提供开发的组织标记以及TRD1和TRD2的诊断参数 到TRD4，这将开发统计和深度学习技术以将它们结合起来并制造它们 可用于年龄依赖性多参数大脑图书馆。