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  4. PhD Studentship in Deep Learning Based Multi-modality Intravascular Imaging for Plaque Assessment

PhD Studentship in AI-based Cardiac Image Computing

Application closing date: 31/03/2021
Start date: September/October 2021
Duration: 3 years
Funding: Available

Applications are invited for a fully-funded PhD Studentship starting in September/October 2021 to undertake research in deep learning based methods for jointly analysing intravascular ultrasound (IVUS) and optical coherence tomography (OCT) images for the assessment of plaque morphology and pathobiology.

Funding is for three years, covering student fees and, in addition, a tax-free stipend starting at £17,609 per annum. This studentship is open to UK residents eligible for 'home' fee status.

IVUS is one of the most established invasive imaging techniques that aids experts to diagnose and treat coronary artery disease. An accurate detection of the lumen and vessel wall borders in IVUS images is important as it allows quantification of the plaque burden, which is essential for optimal treatment planning and for the assessment of the effect of emerging therapies on atherosclerotic evolution. OCT is a novel invasive imaging modality that produces high-resolution cross-sectional images of the vessel wall. These two modalities have complementary strengths but also limitations in characterising lumen architecture and plaque vulnerability.  This PhD aims to develop computational methods that can jointly process the two imaging modalities, namely, IVUS and OCT, enabling a complete and detailed evaluation of coronary atherosclerotic plaques. Recent advances in deep learning models in computer vision will be explored to address challenging tasks related to keyframe detection, alignment, segmentation, and plaque characterisation, in matched IVUS and OCT image datasets, with a special focus on the multi-modal fusion and hybrid imaging.

The PhD will be part of a “mini-CDT” composed of three PhD students studying different aspects of AI-based cardiac image computing.

Applicants should have, or be expected to obtain by the start date, a 1st class or 2:1 degree (or equivalent) in Computer Science, Engineering, Physics, or a related subject. A research publication track record is desired, but not required for the role.

The student will be co-supervised by Dr Qianni Zhang and Dr Christos Bourantas. Zhang is a senior lecturer in Computer Vision and Bourantas is an honorary senior lecturer and consultant in interventional Cardiology at the Barts Health. They will bring together expertise from the computer science and cardiology domains to support this cross-disciplinary research project. Queen Mary is a leading research-intensive Russell Group university, ranked 5th among multi-faculty institutions in the UK for research outputs (Research Excellence Framework 2014), and 110th in the world overall (Times Higher Education World University Rankings 2020).

Informal enquiries regarding the post may be made by email to: Dr Qianni Zhang - qianni.zhang@qmul.ac.uk.

Applications should be made by following the online process at: https://www.qmul.ac.uk/postgraduate/research/subjects/electronic-engineering.html. (“PhD Full-time Electronic Engineering – Semester 1 (September Start)”).

The closing date for applications is: Monday 31st May 2021. Interviews are expected to take place in June 2021.