Transferring principles of solid-state and Laplace NMR to the field of <i>in vivo</i> brain MRI

Abstract

<p><strong>Abstract.</strong> Magnetic resonance imaging (MRI) is the primary method for non-invasive investigations of the human brain in health, disease, and development, but yields data that are difficult to interpret whenever the millimeter-scale voxels contain multiple microscopic tissue environments with different chemical and structural properties. We propose a clinically feasible MRI framework to quantify the microscopic heterogeneity of the living human brain as spatially resolved five-dimensional relaxation-diffusion distributions by augmenting a conventional diffusion-weighted imaging sequence with signal encoding principles from multidimensional solid-state nuclear magnetic resonance (NMR) spectroscopy, relaxation-diffusion correlation methods from Laplace NMR of porous media, and Monte Carlo data inversion. The high dimensionality of the distribution space allows resolution of multiple microscopic environments within each heterogeneous voxel as well as their individual characterization with novel statistical measures that combine the chemical sensitivity of the relaxation rates with the link between microstructure and the anisotropic diffusivity of tissue water.</p>

The effect of gradient nonlinearities on fiber orientation estimates from spherical deconvolution of diffusion magnetic resonance imaging data

“…In addition, various methods have been proposed to estimate the kernel voxel-wise (de Almeida Martins et al, 2019;Kaden et al, 2007;Novikov et al, 2018;Schultz & Groeschel, 2013). The majority of these approaches first factors out the orientational dependency by taking the "powder average" or "spherical mean" to estimate the kernel.…”