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Details of Grant 

EPSRC Reference: EP/R005516/1
Title: Efficient and Robust Assessment of Cardiovascular Disease Using Machine Learning and Ultrasound Imaging
Principal Investigator: King, Dr AP
Other Investigators:
Rinaldi, Dr A
Researcher Co-Investigators:
Project Partners:
Department: Imaging & Biomedical Engineering
Organisation: Kings College London
Scheme: Standard Research
Starts: 01 February 2018 Ends: 31 January 2021 Value (£): 309,998
EPSRC Research Topic Classifications:
Image & Vision Computing Medical Imaging
EPSRC Industrial Sector Classifications:
Healthcare
Related Grants:
EP/R005982/1
Panel History:
Panel DatePanel NameOutcome
11 Sep 2017 HT Investigator-led Panel Meeting - September 2017 Announced
Summary on Grant Application Form
Heart disease is the number one killer in the world. Currently the best way of diagnosing heart disease and planning its treatment is to use a magnetic resonance imaging (MRI) scanner. However, MRI scanners are expensive and not typically used for scanning hearts in most UK hospitals. Therefore, the best diagnosis and treatment are not available to all patients. Currently the most common way of assessing heart disease is through the use of an ultrasound scanner. Although ultrasound has many advantages, it does not have such good image quality as MRI and so there are difficulties associated with its use in heart disease management. If the 'gold standard' quality of assessment from MRI could somehow be made feasible using ultrasound it would have great potential benefits for patients.

This is the aim of this project. We aim to use state-of-the-art machine learning techniques combined with rich multimodal imaging data to produce a computer model of heart disease and its associations with heart shape and motion. By incorporating MRI as well as ultrasound imaging data into the model we can exploit the power of MRI based only on ultrasound imaging. This would make possible a low cost and easy clinical pathway to the best care possible.

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