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Details of Grant 

EPSRC Reference: EP/R003866/1
Title: Adaptive, Multi-scale, Data-Infused Biomechanical Models for Cardiac Diagnostic and Prognostic Assessment
Principal Investigator: Nordsletten, Dr D
Other Investigators:
Researcher Co-Investigators:
Project Partners:
Graz University of Technology Queen Mary University of London
Department: Imaging & Biomedical Engineering
Organisation: Kings College London
Scheme: Standard Research
Starts: 01 January 2018 Ends: 31 December 2021 Value (£): 1,123,553
EPSRC Research Topic Classifications:
Medical Imaging Musculoskeletal system
EPSRC Industrial Sector Classifications:
Healthcare
Related Grants:
Panel History:
Panel DatePanel NameOutcome
18 Sep 2017 Healthcare Technologies Challenge Awards 2 Interviews (Panel B) September 2017 Announced
28 Jun 2017 Healthcare Technologies Challenge Awards 28 June 2017 Announced
Summary on Grant Application Form
Driven by structural and functional abnormalities in the muscle of the heart, Heart Failure (HF) is a complex syndrome affecting around 900,000 people in the UK. HF results in a fundamental reduction in the ability of heart muscle to effectively pump and deliver blood to the body. While this deficiency in pump function is easily observed using medical imaging, dissecting the underlying cause of this reduced performance in terms of its implications for muscle structure and function remain open challenges. Further, predicting how disease will progress or respond to therapy remains an unmet need that would substantially improve patient care.

Using state-of-the art biomechanical modelling, the "Adaptive, Multi-scale, Data-Infused Biomechanical Models for Cardiac Diagnostic and Prognostic Assessment" project will address these challenges by providing a modelling framework for assessment of the heart. Uniting measurements from microscopy, rheology, and medical imaging, this project aims to create biomechanical models that provide detailed information on the structure and function of the heart aiding diagnosis. Further, the platform will provide infrastructure for predictive modelling, simulating the response and adaptation of the heart over time. Biomechanical models will be systematically validated using animal heart models, providing rich data for understanding the biomechanics in vivo and ex vivo. The framework will, further, be directly translated through a novel study in patients with hypertrophic cardiomyopathy, providing a test bed for validation of predictive models of disease progression and response to therapy through virtual surgery.

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