Active shape models—their training and application
Computer Vision and Image Understanding
FIMH '09 Proceedings of the 5th International Conference on Functional Imaging and Modeling of the Heart
MICCAI'10 Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part I
Trials on tissue contractility estimation from cardiac cine MRI using a biomechanical heart model
FIMH'11 Proceedings of the 6th international conference on Functional imaging and modeling of the heart
FIMH'11 Proceedings of the 6th international conference on Functional imaging and modeling of the heart
Constitutive parameter estimation methodology using tagged-MRI data
FIMH'11 Proceedings of the 6th international conference on Functional imaging and modeling of the heart
Statistical atlas of human cardiac fibers: comparison with abnormal hearts
STACOM'11 Proceedings of the Second international conference on Statistical Atlases and Computational Models of the Heart: imaging and modelling challenges
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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.