Ultra-high resolution and multi-shell diffusion MRI of intact ex vivo human brains using kT-dSTEAM at 9.4T

y Diffusion MRI (dMRI) in ex vivo human brain specimens is an important research tool for neuroanatomical investigations and the validation of dMRI techniques. Many ex vivo dMRI applications have benefited from very high dMRI resolutions achievable on small-bore preclinical or animal MRI scanners for small tissue samples. However, the investigation of entire human brains post mortem provides the important context of entire white matter (WM) network systems and entire gray matter (GM) areas connected through these systems. The investigation of intact ex vivo human brains in large bore systems creates challenges due to the limited gradient performance and transmit radio-frequency (B-1+) inhomogeneities, specially at ultra-high field (UHF, 7T and higher). To overcome these issues, it is necessary to tailor ex vivo diffusion-weighted sequences specifically for high resolution and high diffusion-weighting. Here, we present k(T)-dSTEAM, which achieves B-1+ homogenization across whole human brain specimens using parallel transmit (pTx) on a 9.4T MR system. We use k(T)-dSTEAM to obtain multi-shell high b-value and high resolution diffusion-weighted data in ex vivo whole human brains. Isotropic whole brain data can be acquired at high b-value (6000-8000 s/mm(2)) at high resolution (1000 mu m) and at moderate b-value (3000 s/mm(2)) at ultra-high isotropic resolution (400 mu m). As an illustration of the advantages of the ultra-high resolution, tractography across the WM/GM border shows less of the unwanted gyral crown bias, and more high-curvature paths connecting the sulcal wall than at lower resolution. The k(T)-dSTEAM also allows for acquisition of T-1 and T-2 weighted images suitable for estimating quantitative T-1 and T-2 maps. Finally, multi-shell analysis of k(T)-dSTEAM data at variable mixing time (TM) is shown as an approach for ex vivo data analysis which is adapted to the strengths of STEAM diffusion-weighting. Here, we use this gain for multi-orientation modelling and crossing-fiber tractography. We show that multi-shell data allows superior multiple orientation tractography of known crossing fiber structures in the brain stem.