4.7 Article

Disentangling micro from mesostructure by diffusion MRI: A Bayesian approach

Journal

NEUROIMAGE
Volume 147, Issue -, Pages 964-975

Publisher

ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.neuroimage.2016.09.058

Keywords

Microstructural parameters; Axonal density; Diffusion MRI; Microstructure imaging; White matter; Multi-shell dMRI

Funding

  1. Deutsche Forschungsgemeinschaft (German Research Council) [DFG RE 3286/2-1, DFG KI 1089/3-2]

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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.

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