Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.694394
Title: Efficient statistical methods for inference and model selection in diffusion-weighted MRI models
Author: Mott, Lisa
ISNI:       0000 0004 5991 2127
Awarding Body: University of Nottingham
Current Institution: University of Nottingham
Date of Award: 2016
Availability of Full Text:
Access from EThOS:
Full text unavailable from EThOS. Restricted access.
Access from Institution:
Abstract:
Diffusion-Weighted Magnetic Resonance Imaging (DW-MRI) on the brain is a revolutionary method that provides in-vivo access to tissue macrostructure non-invasively (Basser et al., 1994). Recently, DW-MRI has been shown to have great potential in characterising brain microstructure, such as diameter and size distribution of neuronal fibres, features that were available so far only postmortem or through animal studies (Zhang et al., 2011). Using a process known as Tractography the existence of brain connections can be estimated using a set of DW images (Basser et al., 2000). The main aim of this thesis is to develop efficient methods for studying Tractography within a Bayesian framework. In order to characterise the white matter in the brain we focus on the widely used partial volume model (Behrens et al., 2003). We describe methods that are both time and computationally efficient for estimating the parameters of the partial volume model, before reparametrising the model, so that parameter estimation is viable in some special cases. The partial volume model allows for multiple fibre orientations so we develop methodology to choose between the number of white matter fibres in a voxel. We then take into account the uncertainty in the number of fibre orientations and provide a Fully Probabilistic Tractography method as an alternative to existing Tractography algorithms. Finally we look into the Global Tractography model (Jbabdi et al., 2007) and develop efficient methods for inferring connections between brain regions by investigating methods based on Thermodynamic Integration.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.694394  DOI: Not available
Keywords: RC 321 Neuroscience. Biological psychiatry. Neuropsychiatry
Share: