Precision Medicine Doctoral Training Programme

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

Background 

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.

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 leakage.
  2. Compare the best signal-processing-based approach against previous data obtained by conventional methods, and evaluate the results using synthetic and clinical data.
  3. Propose a practical method to analyse subtle BBB leakage in clinical trials.

Hypothesis:

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.

Method:

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.

Training outcomes

  1. Recognise and identify the state-of-art and difficulties of modelling dynamic data from medical images in the study of subtle BBB permeability dysfunction
  2. Identify and familiarise with the state-of-art signal processing methods used in biomedical signal analyses and translate them to medical images
  3. Identify and familiarise with the existent image processing denoising methods and apply them to dynamic MRI acquisitions
  4. Identify and apply the signal processing methods that can be useful in the analysis of DCE-MRI for the study of subtle BBB leakage
  5. Evaluate the most promising signal processing methods for the signal decomposition and analysis of DCE-MRI applied to the study of subtle BBB leakage in regions of interest
  6. Summarise the results of the evaluations made throughout and consolidate the software developments/approaches deemed to be more suitable.
  7. Acquire skills in data management.
  8. Familiarise with and apply clinical research regulations.

References

  1. 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.
  2. Heye, A.K. et al. Tracer kinetic modelling for DCE-MRI quantification of subtle blood–brain barrier permeability. (2016) Neuroimage; 125: 446–455.
  3. 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
  4. 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

 

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  • The deadline for 18/19 applications is 5pm on Wednesday 10th January 2018.
  • Please note all applications for the Precision Medicine DTP should be submitted to University of Edinburgh, even those applying for a project at the University of Glasgow.
  • Applicants must apply to a specific project, ensure you include details of the project you are applying to in Section 4 of your application. We encourage you to contact the primary supervisor prior to making your application.  
  • As you are applying to a specific project, you are not required to submit a Research Proposal as part of your application. 
  • Please ensure you upload as many of the requested documents as possible at the time of submitting your application.