Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany.
Joint Department of Physics, The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust, London, United Kingdom.
Z Med Phys. 2020 Feb;30(1):4-16. doi: 10.1016/j.zemedi.2019.01.003. Epub 2019 Mar 8.
Diffusion anisotropy in diffusion tensor imaging (DTI) is commonly quantified with normalized diffusion anisotropy indices (DAIs). Most often, the fractional anisotropy (FA) is used, but several alternative DAIs have been introduced in attempts to maximize the contrast-to-noise ratio (CNR) in diffusion anisotropy maps. Examples include the scaled relative anisotropy (sRA), the gamma variate anisotropy index (GV), the surface anisotropy (UA), and the lattice index (LI). With the advent of multidimensional diffusion encoding it became possible to determine the presence of microscopic diffusion anisotropy in a voxel, which is theoretically independent of orientation coherence. In accordance with DTI, the microscopic anisotropy is typically quantified by the microscopic fractional anisotropy (μFA). In this work, in addition to the μFA, the four microscopic diffusion anisotropy indices (μDAIs) μsRA, μGV, μUA, and μLI are defined in analogy to the respective DAIs by means of the average diffusion tensor and the covariance tensor. Simulations with three representative distributions of microscopic diffusion tensors revealed distinct CNR differences when differentiating between isotropic and microscopically anisotropic diffusion. q-Space trajectory imaging (QTI) was employed to acquire brain in-vivo maps of all indices. For this purpose, a 15min protocol featuring linear, planar, and spherical tensor encoding was used. The resulting maps were of good quality and exhibited different contrasts, e.g. between gray and white matter. This indicates that it may be beneficial to use more than one μDAI in future investigational studies.
扩散张量成像（DTI）中的各向异性扩散通常通过归一化扩散各向异性指数（DAI）进行量化。最常用的是分数各向异性（FA），但为了最大限度地提高扩散各向异性图中的对比噪声比（CNR），已经引入了几种替代的 DAI。例如，比例相对各向异性（sRA）、伽马变量各向异性指数（GV）、表面各向异性（UA）和晶格指数（LI）。随着多维扩散编码的出现，有可能确定体素中微观扩散各向异性的存在，这在理论上与方向相干性无关。与 DTI 一致，微观各向异性通常通过微观分数各向异性（μFA）进行量化。在这项工作中，除了μFA 之外，还通过平均扩散张量和协方差张量，类似于相应的 DAI，定义了四个微观扩散各向异性指数（μDAI）μsRA、μGV、μUA 和μLI。使用三种有代表性的微观扩散张量分布的模拟显示，在区分各向同性和微观各向异性扩散时，CNR 存在明显差异。q-空间轨迹成像（QTI）用于获取所有指数的大脑体内图谱。为此，使用了具有线性、平面和球形张量编码的 15 分钟协议。得到的图谱质量良好，显示出不同的对比度，例如灰质和白质之间。这表明，在未来的研究中，使用多个μDAI 可能会有所裨益。
