Quantitative White Matter Metrics
: Diffusion Imaging and Advanced Processing for Detailed Investigation of Brain Microstructure

Student thesis: Doctoral ThesisDoctor of Philosophy

Abstract

Diffusion imaging is a non-invasive imaging method which has been successfully applied to study white matter. Most clinical approaches, based on Diffusion Tensor Imaging (DTI), are limited by the simple model of the underlying tissue imposed, failing to reconstruct the diffusion propagator, which fully encodes the displacement of water molecules. To do so, more comprehensive sampling schemes such as Diffusion Spectrum Imaging (DSI) have been developed. In this thesis, I have investigated the effect of different tissue configurations, sampling and processing steps in the performance of DSI. I identified specific configurations where DSI is unable to characterise diffusion without artefacts, namely aliasing caused by fast diffusion components. Furthermore, processing of the diffusion orientation distribution function (ODF) in these environments can lead to generation of spurious fibres in tractography reconstructions. To overcome this, I have applied a novel step in the processing pipeline of DSI, namely a different way of computing the ODF, which consists of restricting the range of integration to probabilities based on the physical displacement of “axonlike” diffusivities. Alternatively, it is possible to use a mathematical representation of the acquired signal, of which the Simple Harmonic Oscillator based Reconstruction and Estimation (SHORE) and Mean Apparent Propagator Magnetic Resonance Imaging (MAP-MRI) are examples. I have here used these methods and further provided optimised acquisitions based on standard propagator metrics. Finally, I have introduced new metrics that use microstructural information available at the different displacement scales, and can facilitate exploration of brain organisation even when no a-priori biophysical model is available.
Date of Award2017
Original languageEnglish
Awarding Institution
  • King's College London
SupervisorFlavio Dell' Acqua (Supervisor) & Gareth Barker (Supervisor)

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