TRD 3: MRI parameters reflecting tissue composition and microstructure

TRD 3：反映组织组成和微观结构的MRI参数 首席研究员：Peter货车Zijl，放射学教授 共同研究者：Xu Li，Manisha Aggarwal，Hye-Young Heo，Jeremiah苏拉姆，Susumu Mori 顾问：Filip Szczepankiewicz（隆德大学） TRD 1和TRD 2侧重于测量实际生理常数和代谢产物的MR方法， 除了信号之外，定量成像生物标志物（QIB）的定义还要更进一步。因此，在TRD 3中，我们 利用MRI的固有能力来探测组织成分和微观结构， 它可以通过大量的MRI现象和参数来访问， 候选生物标志物。MRI体素中的水信号的强度和频率取决于 局部微观场和由组织隔室和分子施加的场差异。此外，本发明还提供了一种方法， 通过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。本TRD的总体目标是 设计脉冲序列和分析方法，以有效量化评估的MRI参数 组织成分和显微结构。我们有以下具体目标： 目的1：开发用于探测组织的房室滤波和扩散编码方法 微观结构 目的2：开发整合磁敏感和扩散张量成像（STI和DTI）用于纤维 纤维束成像，旨在实现高分辨率的白色物质纤维束成像。灰质铁含量 以及血液氧合也将从这些高分辨率的磁化率图像中进行评估 目标3：开发快速多参数采集和分析方法， 目标1和目标2中MR衍生参数的量化以及T1、T2（*）和磁化转移 比率（中期审查）。 所获得的参数将用于合成生成多个图像对比度（合成MRI）， 包括放射科医师熟悉的用于阅读的传统的方法 只能单独购买。八个国家方案将参与优化方法和测试这些方法 生物标志物的潜力。八个专业人员将使用它们来扩大其研究中的信息内容。的 将提供开发的组织标记物以及TRD 1和TRD 2的诊断参数 到TRD 4，它将开发统计和深度学习技术，将它们联合收割机结合起来， 在年龄相关的多参数大脑图谱中可用。

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.