Thesis defence G.A.M. Arkesteijn: MRI

16 February 2018 15:00 - Location: Aula, TU Delft - By: webredactie

Application of Sophisticated Models to Conventional Diffusion-Weighted MRI Data. Promotor 1: Prof.dr.ir. L.J. van Vliet (TNW); Promotor 2: Prof.dr. W.J. Niessen (TNW).

The brain’s white matter mainly consists of neural pathways that connect different parts of the brain. Diffusion-weighted MRI (DW-MRI) is a technique that is particularly suited to image the white matter. In DW-MRI, the MRI signal is sensitized to the diffusion of water in the microstructure. Water diffuses relatively easily along the orientation of neural pathways, but the diffusion is hindered or restricted perpendicular to it: different cellular structures hinder the diffusion. By measuring the diffusion in different orientations, the local diffusion profile of water molecules is obtained which reflects microstructural characteristics of the white matter: e.g. what is the local orientation and density of neural pathways. For instance, if the cell density decreases, the diffusion is less hindered. This is very relevant, for instance to study Alzheimer’s Disease. Researchers hope that such diseases could be detected in an early stage, because changes in the diffusion occur before clinical signs appear. 

The focus of this thesis is on the analysis of conventional DW-MRI data acquired in the context of the Rotterdam Scan Study. This is a prospective population-based cohort study with more than 10.000 participants to investigate causes and progression of neurological diseases in elderly people. Conventional DW-MRI is defined as diffusion data that can be quickly acquired by using only a small number of diffusion measurements. This is relevant since accurate studies of the diffusion are typically very time-consuming. In this thesis (1) a hardware phantom and software simulations are used to enhance the insight in the relation between tissue structure and the DW-MRI signal, and (2) methods are developed to quantify diffusion properties in the brain as accurately and precisely as possible based on conventional DW-MRI data.

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