Signal analysis of dynamic magnetic resonance image acquisitions for the study of subtle blood-brain-barrier changes in small vessel disease
Project Details - Signal analysis of dynamic magnetic resonance image acquisitions for the study of subtle blood-brain-barrier changes in small vessel disease
Supervisor(s): Dr. Maria del Carmen Valdés Hernández, Dr. Javier Escudero Rodríguez, Prof Joanna M. Wardlaw & Prof Rhian M. Touyz
|Centre/Institute: Centre for Clinical Brain Sciences|
There is evidence that subtle breakdown of the blood–brain barrier (BBB) is a pathophysiological component of several diseases, including cerebral small vessel disease and some dementias. Dynamic contrast-enhanced MRI (DCE-MRI) and tracer kinetic modelling is used to assess BBB leakage (Heye et al. 2014). However, in diseases where leakage is subtle, pharmacokinetic models of the BBB leakage are limited since the magnitude and rate of enhancement are low and microvessel surface area, necessary to calculate permeability, is not known (Heye et al. 2016). Also, factors such as scanner signal drift, variations in tissue T1, and artefacts, can introduce systematic errors in estimated permeability, particularly at low permeabilities (Heye et al. 2016). This makes it unclear whether differences in signal enhancement are due to subtle but important BBB abnormality or not. Better methods to separate true signal of BBB leakage from ‘noise’ are needed.
The analysis of the textural features of the tissues pre and post contrast recently emerged as a potential, practical, analysis tool to study BBB disruption (Valdés Hernández et al. 2017). However, although this approach requires further development, it offers a potentially robust way to differentiate subtle levels of BBB dysfunction to improve patient selection and stratification in clinical trials, monitor treatment, and predict outcome.
Our hypothesis is that combining multiscale principal component analysis, denoising, and higher order statistics features extracted from wavelet packet decomposition signal sub-bands, will improve detection of subtle BBB leakage with DCE-MRI.
This project will use data from well-characterised patients with long term outcomes (n=200) and from ongoing studies (n=200 during the PhD) with DCE-MRI data in which conventional BBB analyses are available. The advanced signal processing methods to be tested will include analysis of the power spectrum of the signal (Figure 1), seeking differentiation between common and disease-stage-characteristic spatial patterns and using signal decomposition methods (e.g. empirical mode decomposition, discrete wavelet transform, wavelet packet decomposition) to examine the contrast signal-time trajectory in anatomically and pathologically different brain regions. One of the methods to be tested, Refined Composite Multiscale Dispersion Entropy, is a very fast, powerful method to quantify signal complexity (Azami H and Escudero J et al. 2017), which proved useful to analyse physiological signals through distinguishing different types of dynamics.
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