MRI quantification of blood-brain barrier leakage in the ageing brain

Cerebral small vessel disease, or SVD, refers to processes that lead to dysfunction and damage in cerebral microvessels, and is implicated in ischaemic stroke and vascular dementia. Although the pathophysiology is poorly understood, a subtle breakdown in the blood-brain barrier (BBB) has been implicated as a potential underlying mechanism. BBB breakdown is difficult to measure in-vivo however - Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) is the dominant technique for assessing BBB integrity in clinical populations and is the focus of the work presented in this thesis. In this work, there are two main objectives: 1. Assess and optimise current DCE-MRI processing methods to provide accurate measurements of BBB breakdown. 2. Relate BBB breakdown to other features of SVD: clinical outcomes, imaging markers, and risk factors. To achieve the first objective, simulations were used to estimate the effects of various technical and modelling errors in measured BBB breakdown. By generating a realistic simulation of biological processes during a DCE-MRI sequence, sources of systematic error could be identified along with potential solutions. The implementation of MRI processing recommendations (a slow injection of contrast agent, exclusion of first-pass data from model fitting, and the use of a novel fitting method that better represents underlying biophysics) was found to reduce the sensitivity of calculated DCE-MRI parameters to the effects of variable blood plasma flow, variable water exchange rates, and injection delay by over 90%. Additionally, correction for field inhomogeneities was also found to reduce the error of calculated DCE-MRI parameters. Combining all the suggested processing methods was found to reduce the systematic error of calculated DCE-MRI parameters by up to 97%. These simulations form the basis of an open access framework and include an accessible GUI (1). For the second objective data was obtained from a prospective cohort study of mild stroke patients, and multiple linear regression was used to investigate how regional BBB breakdown is related to various patient factors. Regression models were controlled for several potential confounds and were implemented for both cross-sectional and longitudinal data. It was found that areas of hyperintensity on MRI images (which are indicative of vascular damage) presented lesser BBB breakdown when the severity of imaging markers was greater. Additionally, greater breakdown in the BBB of the basal ganglia is associated with greater disability scores, suggesting that vascular damage in this region may affect motor function and cognition. Risk factors associated with greater BBB breakdown include: age, a diagnosis of hypertension, and a diagnosis of diabetes, although the causality of these relationships is unclear. In summary, this thesis aims to improve the measurement of subtle BBB breakdown using DCE-MRI, and then use the optimised methods to investigate how BBB breakdown relates to clinical outcomes, imaging markers, and risk factors associated with SVD.