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Title: Advanced diffusion MRI for microstructure imaging : theoretical developments
Author: Savickas, A.
ISNI:       0000 0004 7964 7762
Awarding Body: UCL (University College London)
Current Institution: University College London (University of London)
Date of Award: 2016
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An accurate characterization of tissue features at microscopic level is essential for understanding the brain structure or staging a large variety of diseases, such as Alzheimer's, multiple sclerosis or cancer. The aim of microstructure imaging is to provide information related to cellular structure from non-invasive imaging modali¬ties, such as Magnetic Resonance Imaging (MRI). This can be achieved by developing accurate tissue models and relating them to imaging data. Diffusion weighted MRI (DW-MRI) measures the displacement of the water molecules inside the tissue which is sensitive to the configuration of cellular membranes, therefore it provides relevant information for characterizing tissue microstructure. This PhD thesis presents my work on developing novel DW-MRI sequences and tissue models which provide improved sensitivity to cellular features such as pore size and shape. To model the DW-MRI signal, the tissue is regarded as a porous medium with cells described as fluid-filled pores separated by impermeable membranes. The first part of this thesis is concentrated on improving the estimation of intrinsic diffu¬sivity and pore size. Specifically, it analyses a promising class of diffusion sequences, namely oscillating gradients, which measure diffusion on a short time scale and give access to small structures, such as cell nuclei. Additionally, it emphasises the benefits of low-frequency oscillating waveforms over the standard acquisition for estimating axon diameter. The second part of my project focuses on estimating complex tissue features such as pore elongation and size distribution, and it demonstrates that these features are in¬trinsically linked and need to be explicitly modelled for accurate results. Moreover, it shows that pulse sequences with varying gradient orientation, such as double diffusion encoding, are able to separate these effects more effectively than standard sequences. The most recent study combines the benefits of both sequences in an innovative acqui¬sition with double oscillating gradients. This supports estimates of cell size distribution and eccentricity for a wider range of substrates.
Supervisor: Alexander, D. C. ; Drobnjak, I. ; Hawkes, D. Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available