Bayesian Deep Atlases for Cardiac Motion Abnormality Detection from Imaging and Metadata
University of Leeds Department of Engineering
United Kingdom

 

Project description

 

Cardiovascular Diseases (CVDs) cause more than 26% of all deaths in the UK, costing over £15 billion each year. There is good evidence that a large array of CVDs can be diagnosed by an assessment of heart motion abnormalities. The motion of the heart is acquired using a cine CMR imaging technique, generating a sequence of images across a cardiac cycle at various slices through the heart.

 

In this project, we will propose a scalable probabilistic approach for holistic motion atlas modeling from a big population data (UK Biobank Cardiac Imaging) comprising >= 20k patients. The motion will be modelled as the spatiotemporal (3D+t) sequence of the heart shapes across a full cardiac cycle, extracted from cine CMR images.

 

The atlas will be a recurrent deep model that, given a cine sequence, will predict a probabilistic distribution function (pdf) for the next status of the heart. More importantly, the pdf will be conditional on the patient metadata (genomics, age, gender, lifestyle, etc.). Thus, by measuring the spatial deviations from the expected shape at each phase, it will allow accurate and personalized quantification of functional abnormality maps.

 

Entry requirements

Successful candidates will have an excellent first degree in Engineering, Mathematics, Computer Science, or a related discipline. Candidates are expected to have a solid mathematical background, strong programming skills (in C++/Python/Matlab) and a keen interest in high-impact research work. These will be witnessed by the applicant's academic transcript and/or GPA. Previous experience in a research environment and a corresponding track record of publishing results in excellent journals and conferences are valued, but not essential.

 

How to apply

Formal applications for research degree study should be made online through the university's website. Please state clearly in the research information section that the PhD you wish to be considered for is the 'Bayesian Deep Atlases for Cardiac Motion Abnormality Detection from Imaging and Metadata' as well a Dr Ali Gooya as your proposed supervisor.

 

If English is not your first language, you must provide evidence that you meet the University's minimum English Language requirements.

 

We welcome scholarship applications from all suitably-qualified candidates, but UK black and minority ethnic (BME) researchers are currently under-represented in our Postgraduate Research community, and we would therefore particularly encourage applications from UK BME candidates. All scholarships will be awarded on the basis of merit.

 

If you require any further information please contact the Graduate School Office
e: phd@engineering.leeds.ac.uk, t: +44 (0)113 343 8000.


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