Phase-shifted transverse relaxation orientation dependences in human brain white matter.

ABSTRACT

This work aimed to demonstrate an essential phase shift ε 0 for better quantifying R 2 and R 2 * in human brain white matter (WM), and to further elucidate its origin related to the directional diffusivities from standard diffusion tensor imaging (DTI). ε 0 was integrated into a proposed generalized transverse relaxation model for characterizing previously published R 2 and R 2 * orientation dependence profiles in brain WM, and then comparisons were made with those without ε 0 . It was theorized that anisotropic diffusivity direction ε was collinear with an axon fiber subject to all eigenvalues and eigenvectors from an apparent diffusion tensor. To corroborate the origin of ε 0 , R 2 orientation dependences referenced by ε were compared with those referenced by the standard principal diffusivity direction Φ at b-values of 1000 and 2500 (s/mm2 ). These R 2 orientation dependences were obtained from T 2 -weighted images (b = 0) of ultrahigh-resolution Connectome DTI datasets in the public domain. A normalized root-mean-square error ( NRMSE % ) and an F -test were used for evaluating curve-fittings, and statistical significance was considered to be a p of 0.05 or less. A phase-shifted model resulted in significantly reduced NRMSE % compared with that without ε 0 in quantifying various R 2 and R 2 * profiles, both in vivo and ex vivo at multiple B 0 fields. The R 2 profiles based on Φ manifested a right-shifted phase ( ε 0 > 0 ) at two b-values, while those based on ε became free from ε 0 . For all phase-shifted R 2 and R 2 * profiles, ε 0 generally depended on the directional diffusivities by tan - 1 D ⊥ / D ∥ , as predicted. In summary, a ubiquitous phase shift ε 0 has been demonstrated as a prerequisite for better quantifying transverse relaxation orientation dependences in human brain WM. Furthermore, the origin of ε 0 associated with the directional diffusivities from DTI has been elucidated. These findings could have a significant impact on interpretations of prior R 2 and R 2 * datasets and on future research.