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Motion Modelling and Analysis Group

School of Biomedical Engineering and Imaging Sciences,
King's College London

Motion Analysis

Using Machine Learning to Identify Noninvasive Motion-Based Biomarkers of Cardiac Function

(Funded by EPSRC grants EP/K030310/1, EP/K030523/1 and EP/L015226/1)

This project aims to apply state-of-the-art imaging, motion analysis and machine learning techniques to characterise the motion of the heart as it beats. A population-based, spatio-temporal atlas of cardiac motion has been developed that enables subtle variations in cardiac motion to be identified and quantified. This analysis forms the basis for the development of novel noninvasive biomarkers for the stratification of patients with cardiovascular disease. A notable success to date has been the development of biomarkers that can predict response to cardiac resynchronisation therapy with a success rate of 91% (Peressutti et al, Medical Image Analysis), whilst the current clinical selection criteria result in around one third of patients not responding positively to the treatment. More recent work has focused on the incorporation of multi-modal data into the atlas, in the form of MR and US imaging (Puyol-Anton et al, Medical Image Analysis) and the use of the spatio-temporal atlas to identify the characteristic spatial scales of motion signatures (Sinclair et al, Medical Image Analysis).

Tagged MR based motion tracking Tagged MR based motion tracking
Motion-based biomarkers: (left-to-right) tagged MR based motion tracking; motion fields shown on left ventricular mesh.

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