Moss Hunter G, Feiweier Thorsten, Benitez Andreana, Jensen Jens H
Center for Biomedical Imaging, Medical University of South Carolina, Charleston, SC, USA; Department of Neuroscience, Medical University of South Carolina, Charleston, SC, United States of America.
Research & Clinical Translation, Magnetic Resonance, Siemens Healthineers AG, Erlangen, Germany.
Magn Reson Imaging. 2025 Jul;120:110399. doi: 10.1016/j.mri.2025.110399. Epub 2025 Apr 26.
To characterize the complete set of linear rotationally invariant kurtosis measures provided by double diffusion encoding (DDE) MRI, show their utility in distinguishing different types of multiple Gaussian compartment (MGC) models, and demonstrate simplified acquisition and analysis schemes for their estimation.
The lowest order novel information obtainable with DDE MRI can be encapsulated in a six-dimensional kurtosis tensor. The most basic DDE MRI kurtosis measures are rotational invariants that are linear in this tensor while depending on no other physical quantities. We identify four such invariants and show that any others must be linear combinations of these. The invariants are applied to classify MGC models according to whether they include microscopic anisotropy or intercompartmental water exchange. In addition, they are used to investigate the effect of exchange on estimates of the microscopic fractional anisotropy (μFA). Simplified acquisition and analysis schemes for the invariants are proposed and demonstrated with human brain data obtained at 3 T.
For the considered brain regions, the kurtosis invariants are found to be largely consistent with MGC models having microscopic anisotropy. They also indicate that water exchange in gray matter may affect estimates of μFA.
The kurtosis measures can classify MGC models according to whether they have microscopic anisotropy or water exchange, and they can be estimated with simple acquisition and analysis schemes. Measurements of the invariants in brain support the validity of MGC models with microscopic anisotropy and the importance of water exchange for modeling diffusion in gray matter.
对双扩散编码（DDE）磁共振成像（MRI）提供的完整线性旋转不变峰度测量集进行特征描述，展示其在区分不同类型的多高斯 compartment（MGC）模型中的效用，并演示用于其估计的简化采集和分析方案。
DDE MRI可获得的最低阶新信息可封装在一个六维峰度张量中。最基本的DDE MRI峰度测量是该张量中的线性旋转不变量，且不依赖于其他物理量。我们识别出四个这样的不变量，并表明任何其他不变量必定是这些不变量的线性组合。这些不变量被用于根据MGC模型是否包含微观各向异性或隔室间水交换来对其进行分类。此外，它们还被用于研究交换对微观分数各向异性（μFA）估计的影响。提出了这些不变量的简化采集和分析方案，并通过在3T下获得的人脑数据进行了演示。
对于所考虑的脑区，发现峰度不变量在很大程度上与具有微观各向异性的MGC模型一致。它们还表明灰质中的水交换可能会影响μFA的估计。
峰度测量可以根据MGC模型是否具有微观各向异性或水交换来对其进行分类，并且可以通过简单的采集和分析方案进行估计。在脑中对不变量的测量支持了具有微观各向异性的MGC模型的有效性以及水交换在灰质扩散建模中的重要性。
