Towards a biomechanics-based technique for assessing myocardial contractility: an inverse problem approach

Abstract

This work presents the initial development and implementation of a novel 3D biomechanics-based approach to measure the mechanical activity of myocardial tissue, as a potential non-invasive tool to assess myocardial function. This technique quantifies the myocardial contraction forces developed within the ventricular myofibers in response to electro-physiological stimuli. We provide a 3D finite element formulation of a contraction force reconstruction algorithm, along with its implementation using magnetic resonance (MR) data. Our algorithm is based on an inverse problem solution governed by the fundamental continuum mechanics principle of conservation of linear momentum, under a first-order approximation of elastic and isotropic material conditions. We implemented our technique using a subject-specific ventricle model obtained by extracting the left ventricular anatomical features from a set of high-resolution cardiac MR images acquired throughout the cardiac cycle using prospective electrocardiographic gating. Cardiac motion information was extracted by non-rigid registration of the mid-diastole reference image to the remaining images of a 4D dataset. Using our technique, we reconstructed dynamic maps that show the contraction force distribution superimposed onto the deformed ventricle model at each acquired frame in the cardiac cycle. Our next objective will consist of validating this technique by showing the correlation between the presence of low contraction force patterns and poor myocardial functionality.

Keywords:

• myocardial mechanical function
• magnetic resonance imaging
• image registration
• biomechanics
• contraction forces

Acknowledgements

The authors would like to thank Dr Usaf Aladl, Dr Mark Wachowiak, Jaques Milner, John Moore and Chris Wedlake for helpful discussions, advice and technical assistance. We would also like to acknowledge funding for this work provided by the Natural Sciences and Engineering Research Council of Canada and the Canadian Institutes of Health Research. Additional support was provided by the Ontario Research and Development Challenge Funds, the Ontario Innovation Trust, and the Canadian Foundation for Innovation.