Kerkelä, Leevi; (2021) Quantifying Brain Microstructure with Diffusion MRI: An Assessment of Microscopic Anisotropy Imaging. Doctoral thesis (Ph.D), UCL (University College London).

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Abstract

Diffusion-weighted magnetic resonance imaging is routinely used for quantifying microstructural properties of brain tissue in both health and disease for its ability to be sensitive to the displacements of water molecules on a microscopic level. Significant effort has been put into the development of methods that provide more information on tissue microstructure than conventional diffusion tensor imaging. Multidimensional diffusion encoding methods render the signal sensitive to the displacements of water molecules that occur along two or three dimensions and can resolve some degeneracies in data acquired with single diffusion encoding methods that measure diffusion along a single dimension. The aim of this thesis is to study the state-of-the-art microstructural imaging methods and to assess their robustness in estimating microscopic diffusion anisotropy, i.e., the average anisotropy of the microscopic diffusion environments irrespective of their orientation dispersion, prior to their adoption in the wider neuroscience research community and possible deployment in clinical studies. First, a massively parallel Monte Carlo random walk simulator is presented. Second, the reproducibility of three commonly used microstructural models is quantified and the shortcomings of such single diffusion encoding methods in estimating microscopic diffusion anisotropy are addressed. Third, the challenges of estimating microscopic diffusion anisotropy in the human brain using double diffusion encoding are addressed using animal imaging experiments and simulations. The results support the feasibility of double diffusion encoding in human neuroimaging but raise hitherto overlooked precision issues when measuring microscopic diffusion anisotropy. Fourth, the accuracy and precision of microscopic diffusion anisotropy estimation using q-space trajectory encoding, a multidimensional diffusion encoding method specifically developed with the limitations of clinical whole-body scanners in mind, are assessed using imaging experiments and simulations. The results suggest that although broken model assumptions and time-dependent diffusion may bias the estimates, the effect of time-dependent diffusion on the estimated microscopic diffusion anisotropy is small in human white matter.

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