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

This work aimed to demonstrate an essential phase shift e0 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). e0 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 e0. It was theorized that anisotropic diffusivity direction e was collinear with an axon fiber subject to all eigenvalues and eigenvectors from an apparent diffusion tensor. To corroborate the origin of e0, R-2 orientation dependences referenced by e were compared with those referenced by the standard principal diffusivity direction F at b-values of 1000 and 2500 (s/mm2). These R-2 orientation dependences were obtained from T2-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 e0 in quantifying various R-2 and R-2 profiles, both in vivo and ex vivo at multiple B0 fields. The R-2 profiles based on F manifested a right-shifted phase (e0 > 0) at two b-values, while those based on e became free from e0. For all phase-shifted R-2 and R-2 profiles, e0 generally depended on the directional diffusivities by tan (-)1 D. =Dk, as predicted. In summary, a ubiquitous phase shift e0 has been demonstrated as a prerequisite for better quantifying transverse relaxation orientation dependences in human brain WM. Furthermore, the origin of e0 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.