11 General Introduction 1 with MRI, but they will eventually also lead to pathology of the brain parenchyma that can be made visible by MRI.3,6 The most common SVD related brain changes are white matter hyperintensities (WMH), lacunes, microbleeds, enlarged perivascular spaces and also atrophy.6 WMH appear as hyperintense lesions on fluid-attenuated inversion recovery (FLAIR) MRI.6,8 WMH can be categorized into three types based on their location and extent: periventricular, confluent and deep WMH. Periventricular and confluent WMH surround the margins of the lateral ventricles, while deep WMH are punctual lesions located in the deep white matter. In clinical practice assessments of brain MRI scans are usually based on visual inspection and scoring. However, automated segmentation techniques can provide more objective results and are especially useful for research and even more when working with larger datasets. For example, WMH volumes or brain atrophy can be calculated based on automated segmentations.9–12 Different MRI markers of parenchymal changes and their distribution over the brain can be used to discriminate different SVD types.6,13 SVD is a heterogeneous disease including many possible underlying pathologies. Some brain changes in SVD might be the result of impaired clearance of waste products, which has been associated with aging and dementia.14 It is postulated that the brain clearance process is partly driven by the glymphatic system, where cerebrospinal fluid and interstitial fluid ‘flush’ brain tissue and transport metabolic waste out of the parenchyma via perivascular spaces. In cerebral amyloid angiopathy15 and Alzheimer’s dementia, glymphatic function might be impaired.16 Currently, brain clearance related processes are mainly studied invasively in humans, for example by contrast-enhanced MRI following intrathecal injection.17 In this thesis a study including non-invasive MR imaging techniques of the glymphatic system is proposed. 1.3 WMH SHAPE Traditionally, WMH were investigated in research settings by visual rating scales or volume measurements.18 While WMH volume is an objective measure that can be obtained automatically, it is also a rather crude measure. When inspecting MRI scans visually, WMHs can appear very different from each other in shape and location, even if their calculated volumes may be roughly the same. However, measures to objectively quantify such differences that may easily be caught by the eye of a neuroradiologist were lacking. For example, the borders of WMH can in some cases look smooth while in other cases they are irregular and complex. To automatically quantify shape differences of WMHs, several WMH shape markers were introduced previously.19,20 For periventricular/confluent WMH solidity, convexity, concavity index, and fractal dimension specific measures were introduced using the formulas shown
RkJQdWJsaXNoZXIy MTk4NDMw