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Details of Grant 

EPSRC Reference: EP/L505304/1
Title: Atherosclerosis stratification using advanced imaging and computer-based models
Principal Investigator: Botnar, Professor RM
Other Investigators:
Greil, Dr G Hussain, Dr T Alastruey-Arimon, Dr J
Figueroa, Dr C
Researcher Co-Investigators:
Project Partners:
Department: Imaging & Biomedical Engineering
Organisation: Kings College London
Scheme: Technology Programme
Starts: 01 September 2014 Ends: 31 December 2017 Value (£): 309,709
EPSRC Research Topic Classifications:
Med.Instrument.Device& Equip.
EPSRC Industrial Sector Classifications:
Healthcare
Related Grants:
Panel History:  
Summary on Grant Application Form
The goal of the proposed project is to develop a novel tool for atherosclerosis risk stratification. Cardiovascular disease

(CVD) via atherosclerotic plaque rupture (coronary artery disease (CAD) and stroke) is the leading single cause of

morbidity and mortality in the Western world. Vascular atherosclerotic disease is a causative factor in a high percentage of

CVD events. Widely accepted risk markers that allow predicting cardiovascular events such as myocardial infarction and

stroke are currently based on risk factors such as smoking, weight and blood pressure. These lifestyle factors and medical

conditions are derived from population based studies and are linked to an average probability of having a CV event, but do

not measure the individual's personal risk, and are therefore can result in potential overtreatment.

The main deliverable from this project will be a computational tool to assess the grade of atherosclerosis of an individual

person using multi-parametric magnetic resonance imaging (MRI) in combination with biophysical computer models.

Advanced imaging with Magnetic Resonance will be used for plaque burden measurement and plaque component

characterization in order to identify the risk of rupture and the systemic atherosclerosis burden. The team will use exiting

MRI methods in combination with novel markers of plaque vulnerability. These markers include: plaque volume, intraplaque

haemorrhage, lipid content, calcification, endothelial permeability and extracellular volume. In addition, biophysical

models will be used to predict biomechanical properties related to atherosclerotic changes in the vascular system. The

team will investigate and compute markers such as wall shear stress, particle residence time and arterial wall stiffness,

which can give further insight into atherosclerosis development. For the first time, different parameters from modelling and The goal of the proposed project is to develop a novel tool for atherosclerosis risk stratification. Cardiovascular disease

(CVD) via atherosclerotic plaque rupture (coronary artery disease (CAD) and stroke) is the leading single cause of

morbidity and mortality in the Western world. Vascular atherosclerotic disease is a causative factor in a high percentage of

CVD events. Widely accepted risk markers that allow predicting cardiovascular events such as myocardial infarction and

stroke are currently based on risk factors such as smoking, weight and blood pressure. These lifestyle factors and medical

conditions are derived from population based studies and are linked to an average probability of having a CV event, but do

not measure the individual's personal risk, and are therefore can result in potential overtreatment.

The main deliverable from this project will be a computational tool to assess the grade of atherosclerosis of an individual

person using multi-parametric magnetic resonance imaging (MRI) in combination with biophysical computer models.

Advanced imaging with Magnetic Resonance will be used for plaque burden measurement and plaque component

characterization in order to identify the risk of rupture and the systemic atherosclerosis burden. The team will use exiting

MRI methods in combination with novel markers of plaque vulnerability. These markers include: plaque volume, intraplaque

haemorrhage, lipid content, calcification, endothelial permeability and extracellular volume. In addition, biophysical

models will be used to predict biomechanical properties related to atherosclerotic changes in the vascular system. The

team will investigate and compute markers such as wall shear stress, particle residence time and arterial wall stiffness,

which can give further insight into atherosclerosis development. For the first time, different parameters from modelling and The goal of the proposed project is to develop a novel tool for atherosclerosis risk stratification. Cardiovascular disease

(CVD) via atherosclerotic plaque rupture (coronary artery disease (CAD) and stroke) is the leading single cause of

morbidity and mortality in the Western world. Vascular atherosclerotic disease is a causative factor in a high percentage of

CVD events. Widely accepted risk markers that allow predicting cardiovascular events such as myocardial infarction and

stroke are currently based on risk factors such as smoking, weight and blood pressure. These lifestyle factors and medical

conditions are derived from population based studies and are linked to an average probability of having a CV event, but do

not measure the individual's personal risk, and are therefore can result in potential overtreatment.

The main deliverable from this project will be a computational tool to assess the grade of atherosclerosis of an individual

person using multi-parametric magnetic resonance imaging (MRI) in combination with biophysical computer models.

Advanced imaging with Magnetic Resonance will be used for plaque burden measurement and plaque component

characterization in order to identify the risk of rupture and the systemic atherosclerosis burden. The team will use exiting

MRI methods in combination with novel markers of plaque vulnerability. These markers include: plaque volume, intraplaque

haemorrhage, lipid content, calcification, endothelial permeability and extracellular volume. In addition, biophysical

models will be used to predict biomechanical properties related to atherosclerotic changes in the vascular system. The

team will investigate and compute markers such as wall shear stress, particle residence time and arterial wall stiffness,

which can give further insight into atherosclerosis development. For the first time, different parameters from modelling and imaging will be integrated into one clinical tool for comprehensive and individual risk stratification on different assessment

levels.
Key Findings
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Summary
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