Signal analysis of dynamic magnetic resonance image acquisitions for the study of subtle blood-brain-barrier changes in small vessel disease

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SummaryThere 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 are 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 permeability (Heye et al. 2016). This issue makes it unclear whether differences in signal enhancement are due to subtle but critical BBB abnormality or not. Better methods to separate the actual 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 an outcome.Aims:1. Using Texture Analysis and other methods of signal processing to re-evaluate DCE-MRI data from patients well-characterised for cognition, stroke and small vessel disease, to differentiate severities of BBB leakage2. Compare the best signal-processing-based approach against previous data obtained by conventional methods, and evaluate the results using synthetic and clinical data3. Propose a practical approach to analyse subtle BBB leakage in clinical trials. Hypothesis:We hypothesise 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. Method:The 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 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. References- Heye, A. K. et al. Assessment of blood-brain barrier disruption using dynamic contrast-enhanced MRI. A systematic review. (2014) Neuroimage Clin; 6: 262-274- Heye, A.K. et al. Tracer kinetic modelling for DCE-MRI quantification of subtle blood-brain barrier permeability. (2016) Neuroimage; 125: 446-455- Valdés Hernández et al. Application of Texture Analysis to Study Small Vessel Disease and Blood-Brain Barrier Integrity. (2017) Front Neurol. https://doi.org/10.3389/fneur.2017.00327- Azami, H, Rostaghi, M, Abásolo, D & Escudero, J. Refined Composite Multiscale Dispersion Entropy and its Application to Biomedical Signals. (2017) IEEE Transactions on Biomedical Engineering (e-pub ahead of print). DOI:10.1109/TBME.2017.2679136