Abstract
We have developed finite element modelling techniques to semi-automatically generate personalised biomechanical models of the human left ventricle (LV) based on cardiac magnetic resonance images. Geometric information of the LV throughout the cardiac cycle was derived via semi-automatic segmentation using non-rigid image registration with a pre-segmented image. A reference finite element mechanics model was automatically fitted to the segmented LV endocardial and epicardial surface data at diastasis. Passive and contractile myocardial mechanical properties were then tuned to best match the segmented surface data at end-diastole and end-systole, respectively. Global and regional indices of myocardial mechanics, including muscle fibre stress and extension ratio were then quantified and analysed. This mechanics modelling framework was applied to a healthy human subject and a patient with non-ischaemic heart failure. Comparison of the estimated passive stiffness and maximum activation level between the normal and diseased cases provided some preliminary insight into the changes in myocardial mechanical properties during heart failure. This automated approach enables minimally invasive personalised characterisation of cardiac mechanical function in health and disease. It also has the potential to elucidate the mechanisms of heart failure, and provide new quantitative diagnostic markers and therapeutic strategies for heart failure.
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Wang, V.Y. et al. (2013). Automated Personalised Human Left Ventricular FE Models to Investigate Heart Failure Mechanics. In: Camara, O., Mansi, T., Pop, M., Rhode, K., Sermesant, M., Young, A. (eds) Statistical Atlases and Computational Models of the Heart. Imaging and Modelling Challenges. STACOM 2012. Lecture Notes in Computer Science, vol 7746. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-36961-2_35
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DOI: https://doi.org/10.1007/978-3-642-36961-2_35
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