Disentangling micro from mesostructure by diffusion MRI: A Bayesian approach

Diffusion-sensitized magnetic resonance imaging probes the cellular structure of the human brain, but the primary microstructural information gets lost in averaging over higher-level, mesoscopic tissue organization such as different orientations of neuronal fibers. While such averaging is inevitable due to the limited imaging resolution, we propose a method for disentangling the microscopic cell properties from the effects of mesoscopic structure. We further avoid the classical fitting paradigm and use supervised machine learning in terms of a Bayesian estimator to estimate the microstructural properties. The method finds detectable parameters of a given microstructural model and calculates them within seconds, which makes it suitable for a broad range of neuroscientific applications. •Disentanglement of microstructural properties of neurites from their orientation distribution.•Microstructure estimation from clinical feasible dMRI, including fast protocols (as few as 28 diffusion weighting directions).•Computation time of seconds.•In-vivo results are consistent with existing anatomical knowledge.