TRD 3: MRI parameters reflecting tissue composition and microstructure

TRD3：反映组织成分和微观结构的 MRI 参数 首席研究员：Peter van Zijl，放射学教授 联合研究员：徐力、Manisha Aggarwal、Hye-Young Heo、Jeremias Sulam、Susumu Mori 顾问：Filip Szczepankiewicz（隆德大学） TRD 1 和 2 侧重于测量实际生理常数和代谢物的 MR 方法 信号，定量成像生物标志物 (QIB) 的定义更进一步。因此，在 TRD3 中我们 利用 MRI 的固有能力来探测组织成分和微观结构、 可以通过多种 MRI 现象和参数来访问，这些现象和参数可以视为 候选生物标志物。 MRI 体素中水信号的强度和频率取决于 局部微观场和组织区室和分子造成的场差异。此外， MRI 测量的水的运动受到隔室大小和渗透性的影响，这可能 疾病的变化，因此包含潜在的生物标志物信息。该 TRD 的总体目标是 设计脉冲序列和分析方法，以有效量化评估 MRI 参数 组织成分和微观结构。我们有以下具体目标： 目标 1：开发区室过滤和扩散编码方法来探测组织 微观结构。 目标 2：开发光纤集成磁化率和扩散张量成像（STI 和 DTI） 纤维束成像，旨在体内高分辨率白质纤维纤维束成像。灰质铁含量 血氧饱和度也将通过这些高分辨率磁敏感图像进行评估 目标 3：开发同时进行快速多参数采集和分析方法 目标 1 和 2 中 MR 衍生参数的量化以及 T1、T2(*) 和磁化传递 比率（地铁）。 获得的参数将用于综合生成多个图像对比（合成MRI）， 包括放射科医生熟悉阅读且目前可以使用的常规内容 仅单独获得。八个 CP 将参与优化方法并测试这些方法 生物标志物潜力。八名 SP 将使用它们来扩展其研究中的信息内容。的 开发的组织标记物以及 TRD1 和 TRD2 的诊断参数将可供使用 TRD4 将开发统计和深度学习技术，将它们结合起来并使它们 可在年龄相关的多参数脑图谱中找到。

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.