Toward a quantitative assessment of diffusion anisotropy

TLDR

Diffusion anisotropy indices derived from two or three perpendicular diffusion coefficients vary with rotation, making them rotationally variant. The study aims to develop rotationally invariant anisotropy indices from the full diffusion tensor and introduce a new intervoxel index that averages neighboring tensor inner products. Rotationally invariant indices are computed from the complete diffusion tensor, and the intervoxel index is obtained by locally averaging inner products between diffusion tensors of adjacent voxels. These indices reveal that anisotropy is highly variable across white matter, with parallel diffusivity nearly ten times higher than perpendicular values, reduced anisotropy in less coherent fiber patterns, and that while RI indices remain noise‑sensitive, the lattice intervoxel index shows low error variance and reduced bias compared to other RI measures.

Abstract

Abstract Indices of diffusion anisotropy calculated from diffusion coefficients acquired in two or three perpendicular directions are rotationally variant. In living monkey brain, these indices severely underestimate the degree of diffusion anisotropy. New indices calculated from the entire diffusion tensor are rotationally invariant (RI). They show that anisotropy is highly variable in different white matter regions depending on the degree of coherence of fiber tract directions. In structures with a regular, parallel fiber arrangement, water diffusivity in the direction parallel to the fibers (D | ≈ 1400–1800 × 10 −6 mm 2 /s) is almost 10 times higher than the average diffusivity in directions perpendicular to them ((D + D⊥′)/2 ≈ 150–300 × 10 −6 mm 2 /s), and is almost three times higher than previously reported. In structures where the fiber pattern is less coherent (e.g., where fiber bundles merge), diffusion anisotropy is significantly reduced. However, RI anisotropy indices are still susceptible to noise contamination. Monte Carlo simulations show that these indices are statistically biased, particularly those requiring sorting of the eigenvalues of the diffusion tensor based on their magnitude. A new intervoxel anisotropy index is proposed that locally averages inner products between diffusion tensors in neighboring voxels. This “lattice” RI index has an acceptably low error variance and is less susceptible to bias than any other RI anisotropy index proposed to date.