Abstract
Magnetic resonance imaging (MRI) is the primary method for noninvasive 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 novel 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. The proposed framework is demonstrated on a healthy volunteer using both exhaustive and clinically viable acquisition protocols.
Highlights
The structure of the brain is affected by both disease and normal development over a wide range of length scales
Voxels encompassing either white matter (WM), gray matter (GM), or cerebrospinal fluid (CSF) are all characterized by clearly distinct signal patterns
The observed differences can be used to infer the gross R2–D properties of the various cerebral constituents: WM signals are highly sensitive to both b and (, ), indicative of anisotropic diffusion along coherently aligned microscopic domains; GM signal patterns are rather insensitive to b and (, ), consistent with isotropic diffusion; and CSF data decays quickly with increasing b while remaining mostly unaffected by the other acquisition variables, features that suggest an isotropic medium characterized by relatively low R2 values
Summary
The structure of the brain is affected by both disease and normal development over a wide range of length scales. To measure and map the cellular architecture and molecular composition of the living human brain is a challenging experimental endeavor that promises far-reaching implications for both clinical diagnosis and our understanding of normal brain function. Magnetic resonance imaging (MRI) methods have been crucial for the progress of neuroanatomical studies (Lerch et al, 2017). Most clinical MRI applications rely on detecting 1H nuclei of water molecules to produce three-dimensional images with a spatial resolution on the millimeter scale. Even though the attainable resolution is clearly insufficient for direct observation of individual cells, chemical and microstructural features can be investigated by probing their effect on magnetic resonance observables such as nuclear relaxation rates (Halle, 2006) and the translational diffusivity (Le Bihan, 1995) of water. Current quantitative relaxation (Tofts, 2003) and diffusion (Jones, 2010) MRI observables are exquisitely sensitive to the cellular processes associated with knowledge acquisition (Zatorre et al, 2012), neuropsychiatric disorders (Kubicki et al, 2007), and different tumor types (Nilsson et al, 2018a), but suffer from poor specificity, and the same ex-
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