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: 10614612
• 负责人: Peter CM Van Zijl
• 金额: $ 19.65万
• 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/10614612
• 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; Maps; Measurement; Measures; Metabolic; Methods; Microscopic; Molecular; Motion; Myelin; Organ; Pathogenicity; Permeability; Phase; Physiologic pulse; Physiological; Predisposition; Principal Investigator; Process; Property; Proteins; Protons; Radiology Specialty; Reading; Relaxation; Reporting; Research Personnel; Resolution; Resources; Rotation; Shapes; Signal Transduction; Structure; Technology; Testing; Therapeutic Intervention; Time; Tissues; Universities; Variant; Water; age related; 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：反映组织成分和微结构的MRI参数 首席研究员：Peter van Zijl，放射学教授 共同调查人员：许莉，马尼莎·阿加瓦尔，许惠英，杰里米亚斯·苏拉姆，苏苏木·森苏苏 顾问：Filip Szczepankiewicz(隆德大学) 而TRDS 1和2侧重于测量实际生理常数和代谢物的MR方法 信号，定量成像生物标记物(QIB)的定义要深入得多。在TRD3中，我们因此 利用MRI固有的力量探测组织成分和微观结构，其特点 它可以通过大量的MRI现象和参数访问，这些现象和参数可以被视为 候选生物标志物。磁共振体素中水信号的强度和频率取决于 局部微观场和由组织隔间和分子施加的场差异。此外, 核磁共振测量的水的运动受隔间大小和渗透率的影响，这可能 疾病的变化，因此包含潜在的生物标志物信息。这项TRD的总体目标是 设计脉冲序列和分析方法以有效量化MRI参数 组织成分和显微结构。我们有以下具体目标： 目的1：发展用于探测组织的隔室过滤和扩散编码方法 微观结构。 目的2：纤维磁化率和扩散张量综合成像(STI和DTI)的发展 针对活体高分辨率白质纤维纤维束成像。灰质铁含量 从这些高分辨率的敏感性图像中也可以评估血液的氧合情况 目标3：开发快速多参数采集和分析方法 AIMS 1和2加上T1，T2(*)中MR衍生参数的量化和磁化转移 比率(港铁)。 所获得的参数将用于综合生成多个图像对比度(合成MRI)， 包括放射科医生熟悉阅读的传统文档，目前可以 只能单独收购。八名CP将参与优化方法和测试这些方法 生物标记物的潜力。八个SP将使用它们来扩展他们研究中的信息内容。这个 开发的组织标记以及TRD1和TRD2的诊断参数将可用 到TRD4，它将开发统计和深度学习技术来将它们结合起来，并使它们 提供与年龄相关的多参数脑图集。