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
