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Details of Grant 

EPSRC Reference: EP/F059175/1
Title: Grand Challenge: Translating Biomedical Modelling into the Heart of the Clinic
Principal Investigator: Razavi, Professor R
Other Investigators:
Sermesant, Dr MOV Rhode, Dr KS Penney, Dr GP
Hawkes, Professor D Nagel, Professor E Schaeffter, Professor TR
Researcher Co-Investigators:
Project Partners:
Department: Imaging & Biomedical Engineering
Organisation: Kings College London
Scheme: Standard Research
Starts: 07 August 2008 Ends: 09 November 2009 Value (£): 110,645
EPSRC Research Topic Classifications:
Image & Vision Computing Medical science & disease
EPSRC Industrial Sector Classifications:
Healthcare
Related Grants:
EP/F059361/1 EP/F059140/1
Panel History:
Panel DatePanel NameOutcome
14 Dec 2007 EPSRC/MRC Info Driven Health 1 Sift Panel (ENG) Deferred
28 Feb 2008 Information Driven Health Interview panel Announced
Summary on Grant Application Form
Heart disease is the most common cause of illness and death in the western world. Each year it causes over 208,000 deaths in the UK including nearly half of all non-accidental deaths. Improving the treatment of heart disease is one of the main priorities of the NHS. The aim of this project is to work out the best way we can improve the diagnosis and treatment of heart disease by using state of the art computer models that have been built and personalised for each patient using information such as scans of their heart. These computer models can predict what goes on in the heart by using detailed mathematical formulae which copy they way the heart behaves all the way down to the millions of cells that make up the heart. This works is now possible because of the rapid increases in computer power which means they can quickly do the large number of calculations needed to make the models work. Also because we can do very detailed three-dimensional scans of the heart in patients at the same time as measure the electrical patterns and blood pressure inside the heart we are putting in very accurate information into the models and so can get accurate answers out. These answers have the potential of telling us which patients are going to be at risk of a heart attack or what's the best ways to treat patients whose hearts are not working well. By getting this research to work and implemented in hospital we should be able to substantially improve the treatment of patients with heart disease and even reduce the costs to the NHS.
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