Contrast-to-noise ratio analysis of microscopic diffusion anisotropy indices in q-space trajectory imaging

• Published in: arXiv.org 2020-04
• Main Authors: Martin, Jan, Endt, Sebastian, Wetscherek, Andreas, Tristan Anselm Kuder, Doerfler, Arnd, Uder, Michael, Hensel, Bernhard, Laun, Frederik Bernd
• Format: Article
• Language: English
• Subjects: Anisotropy; Covariance; Diffusion; Imaging; Mathematical analysis; Tensors
• ISSN: 2331-8422
• DOI: 10.48550/arxiv.2004.00892

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 (UAsurf), 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 (uFA). In this work, in addition to the uFA, the four microscopic diffusion anisotropy indices (uDAIs) usRA, uGV, uUAsurf, and uLI 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 15 min 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 uDAI in future investigational studies.