Identifying new diabetic retinopathy biomarkers based on Optical Coherence Tomography Angiography (OCT-A), advanced image processing, and computational modelling

Background
Diabetic retinopathy (DR) is the leading cause of visual loss in developed countries worldwide. Previous studies have reported haemodynamic changes in the diabetic eye that precede clinically evident pathological alterations of the retinal microvasculature. Therefore, new methods that allow greater understanding of these early haemodynamic changes may empower earlier detection of DR helping to limit vision loss.

Optical Coherence Tomography Angiography (OCT-A) [1] is a non-invasive technique for retinal imaging. OCT-A can resolve the microvasculature of the eye to a level of detail never seen before. Only very recently, OCT-A commercial products have started to be utilised in clinical centres. One of the first OCT-A devices installed in the UK is hosted in Edinburgh at the Clinical Research Imaging Centre (CRIC). CRIC’s Image Analysis Laboratory Manager, Dr MacGillivray, is one of the co-supervisors of this project and works closely with a team at Queen’s University in Belfast, where a second OCT-A device is hosted. Furthermore, co-supervisors Prof. Dhillon and Dr MacGillivray have over 12 years’ experience developing a successful research programme around retinal image analysis and are behind the software package VAMPIRE used in research centres worldwide [2].

In recent work [3], co-supervisor Dr Bernabeu and colleagues showed that it is possible to build computational blood flow models from high-resolution images of the parafoveal region of the retina (of paramount importance for sharp central vision and visual detail). These computational models can provide a detailed haemodynamic characterisation of the region. This will enable us to run longitudinal studies featuring patients with diabetes and identify flow- based early indicators of the structural changes that lead to visual loss in advanced DR stages. We now have a unique opportunity to combine this technology with the OCT-A datasets at Edinburgh and Belfast and the VAMPIRE toolkit in order to realise the full potential of this patient-specific modelling approach to achieving Precision Medicine for eye care.

The supervisory team brings together a unique set of skills in order to lead a programme of research of high calibre: Prof. Andrew Morris (Health Informatics, Data Science), Dr Miguel O. Bernabeu (computational modelling in Biology and Medicine), Dr Tom MacGillivray (multimodal image acquisition and retinal image processing), and Prof. Baljean Dhillon (Clinical Ophthalmology and Brain Sciences).

Aims
In this project, the student will bring together cutting edge image processing and computational modelling methods in order to characterise the early changes in microvascular haemodynamics associated with DR. The project will take advantage of the new OCT-A datasets in Edinburgh and Belfast, which are world-leading. The main objective is to investigate clinically relevant diabetic retinopathy biomarkers and design the computational pipelines necessary to facilitate their use in future large-scale clinical studies.

Training outcomes
The student will receive state-of-the-art training in the core disciplines of image analysis, computational modelling, statistical methods, and data science while gaining expert knowledge in the context of diabetic retinopathy. The student will develop the essential soft and domain-specific skills necessary to design and implement novel quantitative and computational methods that could solve challenging problems across the entire spectrum of vascular medicine both in academic and industrial settings.

Application
This MRC DTP programme is joint between the Universities of Edinburgh and Glasgow. You will be registered at the host institution of the primary supervisor detailed in your project selection.

You can apply here via the University of Glasgow: http://www.gla.ac.uk/research/opportunities/howtoapplyforaresearchdegree/
Within the application, at the programme of study search field option, please select ‘MRC DTP in Precision Medicine’.

Please note that, in step 6 within the online application process, you are asked to detail supervisor/project title information. Please ensure that you clearly detail this information from the information provided within this abstract advert. Within the research area text box area, you can also add further details if necessary.

Please ensure that all of the following supporting documents are uploaded at point of application:
• CV/Resume
• Degree certificate (if you have graduated prior to 1 July 2016)
• Language test (if relevant)
• Passport
• Personal statement
• Reference 1 (should be from an academic who has a knowledge of your academic ability from your most recent study/programme)
• Reference 2 (should be from an academic who has a knowledge of your academic ability)
• Transcript

For more information about Precision Medicine at the University of Edinburgh, visit http://www.ed.ac.uk/medicine-vet-medicine/postgraduate/research-degrees/phds/precision-medicine