Automatic Analysis of Brain Tissue and Structural Connectivity in MRI

Studies of the brain using magnetic resonance imaging (MRI) can provide insights in physiology and pathology that can eventually aid clinical diagnosis and therapy monitoring. MRI data acquired in these studies can be difficult, as well as laborious, to interpret and analyze by human observers. Moreover, analysis by human observers can hamper the reproducibility by both inter- and intra-observer variability. These studies do, therefore, require accurate and reproducible quantitative image analysis techniques to optimally benefit from the valuable information contained in the MRI data. In this thesis, we focus on the development and evaluation of quantitative analysis techniques for brain MRI data. In the first part of this thesis, we focus on automatic brain tissue and white matter lesion (WML) segmentation. We propose an automatic WML segmentation method based on fluid-attenuated inversion recovery (FLAIR) scans that can be added as an extension to brain tissue segmentation methods. We optimize and evaluate a previously proposed automatic brain tissue segmentation method in combination with the WML segmentation extension. We compare the accuracy and reproducibility of this newly developed segmentation framework to several other methods, some of which are publicly available. Additionally, we compare two brain tissue segmentation methods on the segmentation of longitudinal brain MRI data. The second part of this thesis is about structural brain connectivity based on diffusion MRI data. We propose a framework for analysis of structural connectivity in large groups of subjects. Structural connectivity is established using minimum cost paths based on the diffusion weighted images and is summarized in brain networks. Using statistical methods, we demonstrate that the obtained networks contain information regarding subject age, white matter lesion load and white matter atrophy. Finally, we evaluate the reproducibility of the proposed brain connectivity framework.

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