Application of Sophisticated Models to Conventional Diffusion-Weighted MRI Data

Abstract

The brain’s white matter mainly consists of (myelinated) axons that connect different parts of the brain. Diffusion-weighted MRI (DW-MRI) is a technique that is particularly suited to image this white matter. The MRI signal in DW-MRI is sensitized to diffusion of water in the microstructure by introducing strong bipolar gradients in the MRI pulse sequence. By measuring the diffusion in different directions, the local diffusion profile of water molecules is obtained which reflects microstructural characteristics of the white matter. 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 of neurological disease in elderly people. Conventional DW-MRI is defined as diffusion data acquired with a single diffusion-weighting factor and a small number of diffusion-sensitizing gradient orientations. The objectives of this thesis are (1) to enhance our insight in the relation between tissue structure and the DW-MRI signal from conventional DW-MRI sequences, and (2) to develop methods to quantify diffusion properties in the brain as accurately and precisely as possible based on conventional DW-MRI data. To gain insight into the relation between tissue structure and the DW-MRI signal, simulated DW-MRI signals based on Monte Carlo simulations of spins between randomly packed cylinders are compared to experimentally acquired data from a hardware phantom. The hardware phantom consists of solid fibers and acts as a model for the extra-axonal diffusion. The simulated DW-MRI signal is in good agreement with the experimentally acquired data. Furthermore, simulations show that the DW-MRI signal from spins between randomly packed cylinders is relatively independent of the cylinder diameter for b-values up to 1500 s/mm2. For b-values higher than 1500 s/mm2, substrates with a smaller cylinder diameter yield a larger attenuation of the diffusion-weighted signal (chapter 2). Conventional DW-MRI data is commonly analyzed with a technique known as diffusion tensor imaging. Here, thewater diffusion profile is modelled by a 3D Gaussian diffusion profile. However, in white matter structures in close proximity to the cerebrospinal fluid (CSF) the use of the single diffusion tensor model is inappropriate. A novel framework is introduced to analyze white matter structures adjacent to the CSF. In this framework a constrained two-compartment diffusion model is fit to the data in which the CSF is explicitly modeled with a free water diffusion compartment. The proposed diffusion statistics are shown to be relatively independent of partial volume effects with CSF and are applied to study ageing in the fornix, a small white matter structure bordering the CSF (chapter 3). A significant part of the white matter constitutes of ‘crossing fibers’, whereby two or more white matter tracts contribute to the DW-MRI signal in a voxel. The single diffusion tensor model cannot adequately describe the data in such voxels. To solve this issue a fiber orientation atlas and a model complexity atlas were used to analyze conventional DW-MRI data with a simple crossing fibers model, namely the ball-and-sticks model. It is shown that the application of a fiber orientation atlas and a model complexity atlas can significantly improve the reproducibility and sensitivity of diffusion statistics in a voxel-based analysis (chapter 4). Finally, a framework is proposed that aims to specifically improve the analysis of longitudinal DW-MRI data. In this framework the ball-and-sticks model is fit simultaneously to multiple scans of the same subject. The orientations of the sticks are constrained to be the same over different scans, while all other parameters are estimated separately for each scan. The use of this framework is shown to increase the precision of estimated ball-and-sticks model parameters in longitudinal DW-MRI studies (chapter 5). In conclusion, this thesis describes frameworks to enhance the accuracy or precision of estimated diffusion properties of the white matter by applying sophisticated diffusion models to conventional DW-MRI data. We anticipate that many diffusion MRI studies may benefit from the work described in this thesis.