Abstract
The brain’s white matter mainly consists of (myelinated) axons that connect different parts of the brain. Diffusionweighted MRI (DWMRI) is a technique that is particularly suited to image this white matter. The MRI signal in DWMRI 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 DWMRI data acquired in the context of the Rotterdam Scan Study. This is a prospective populationbased cohort study with more than 10.000 participants to investigate causes of neurological disease in elderly people. Conventional DWMRI is defined as diffusion data acquired with a single diffusionweighting factor and a small number of diffusionsensitizing gradient orientations. The objectives of this thesis are (1) to enhance our insight in the relation between tissue structure and the DWMRI signal from conventional DWMRI sequences, and (2) to develop methods to quantify diffusion properties in the brain as accurately and precisely as possible based on conventional DWMRI data.
To gain insight into the relation between tissue structure and the DWMRI signal, simulated DWMRI 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 extraaxonal diffusion. The simulated DWMRI signal is in good agreement with the experimentally acquired data. Furthermore, simulations show that the DWMRI signal from spins between randomly packed cylinders is relatively independent of the cylinder diameter for bvalues up to 1500 s/mm^{2}. For bvalues higher than 1500 s/mm^{2}, substrates with a smaller cylinder diameter yield a larger attenuation of the diffusionweighted signal (chapter 2).
Conventional DWMRI 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 twocompartment 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 DWMRI 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 DWMRI data with a simple crossing fibers model, namely the ballandsticks 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 voxelbased analysis (chapter 4).
Finally, a framework is proposed that aims to specifically improve the analysis of longitudinal DWMRI data. In this framework the ballandsticks 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 ballandsticks model parameters in longitudinal DWMRI 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 DWMRI data. We anticipate that many diffusion MRI studies may benefit from the work described in this thesis.
The focus of this thesis is on the analysis of conventional DWMRI data acquired in the context of the Rotterdam Scan Study. This is a prospective populationbased cohort study with more than 10.000 participants to investigate causes of neurological disease in elderly people. Conventional DWMRI is defined as diffusion data acquired with a single diffusionweighting factor and a small number of diffusionsensitizing gradient orientations. The objectives of this thesis are (1) to enhance our insight in the relation between tissue structure and the DWMRI signal from conventional DWMRI sequences, and (2) to develop methods to quantify diffusion properties in the brain as accurately and precisely as possible based on conventional DWMRI data.
To gain insight into the relation between tissue structure and the DWMRI signal, simulated DWMRI 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 extraaxonal diffusion. The simulated DWMRI signal is in good agreement with the experimentally acquired data. Furthermore, simulations show that the DWMRI signal from spins between randomly packed cylinders is relatively independent of the cylinder diameter for bvalues up to 1500 s/mm^{2}. For bvalues higher than 1500 s/mm^{2}, substrates with a smaller cylinder diameter yield a larger attenuation of the diffusionweighted signal (chapter 2).
Conventional DWMRI 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 twocompartment 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 DWMRI 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 DWMRI data with a simple crossing fibers model, namely the ballandsticks 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 voxelbased analysis (chapter 4).
Finally, a framework is proposed that aims to specifically improve the analysis of longitudinal DWMRI data. In this framework the ballandsticks 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 ballandsticks model parameters in longitudinal DWMRI 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 DWMRI data. We anticipate that many diffusion MRI studies may benefit from the work described in this thesis.
Original language  English 

Qualification  Doctor of Philosophy 
Awarding Institution 

Supervisors/Advisors 

Thesis sponsors  
Award date  16 Feb 2018 
Print ISBNs  9789463323154 
DOIs  
Publication status  Published  2018 
Keywords
 DTI
 DWMRI
 dMRI
 Diffusion
 Brain