High-fidelity, high-spatial-resolution diffusion MRI of the ex-vivo whole human brain on the 3T Connectom scanner using structured low-rank EPI ghost correction

Abstract

Diffusion MRI (dMRI) of whole, intact, fixed postmortem human brain at high spatial resolution serves as key bridging technology for 3D mapping of structural connectivity and tissue microstructure at the mesoscopic scale. Ex vivo dMRI offers superior spatial resolution compared to in vivo dMRI but comes with its own technical challenges due to the significantly reduced T2 relaxation times and decreased diffusivity incurred by tissue fixation. The altered physical properties of fixed tissue necessitate the use of alternative acquisition strategies to preserve SNR and achieve sufficient diffusion weighting. Multi-shot or segmented 3D echo planar imaging (EPI) sequences have been used to shorten echo times (TEs) with reduced distortions from field inhomogeneity and eddy currents on small-bore MR scanners and have been adopted for high b-value dMRI of ex vivo whole human brain specimens. The advent of stronger gradients on human MRI scanners has led to improved image quality and a wider range of diffusion-encoding parameters for dMRI but at the cost of more severe eddy currents that result in spatial and temporal variations in the background magnetic field, which cannot be corrected for using standard vendor-provided ghost correction solutions. In this work, we show that conventional ghost correction techniques based on navigators and linear phase correction may be insufficient for EPI sequences using strong diffusion-sensitizing gradients in ex vivo dMRI experiments, resulting in orientationally biased dMRI estimates. This previously unreported problem is a critical roadblock in any effort to leverage scanners with ultra-high gradients for high-precision mapping of human neuroanatomy at the mesoscopic scale. We propose an advanced reconstruction method based on structured low-rank matrix modeling that reduces the ghosting substantially. We show that this method leads to more accurate and reliable dMRI metrics, as exemplified by diffusion tensor imaging and high angular diffusion imaging analyses in distributed neuroanatomical areas of fixed whole human brain specimens. Our findings advocate for the use of advanced reconstruction techniques for recovering unbiased metrics from ex vivo dMRI acquisitions and represent a crucial step toward making full use of strong diffusion-encoding gradients for neuroscientific studies seeking to study brain structure at multiple spatial scales.