Journal
BIOMECHANICS AND MODELING IN MECHANOBIOLOGY
Volume 15, Issue 5, Pages 1121-1139Publisher
SPRINGER HEIDELBERG
DOI: 10.1007/s10237-015-0748-z
Keywords
Cardiac mechanics; Parameter estimation; 3D tagged MRI; Patient-specific modelling
Categories
Funding
- BHF New Horizons Program [NH/11/5/29058]
- Engineering and Physical Sciences Research Council [EP/H046410/1, EP/K030310/1]
- Wellcome Trust EPSRC Centre of Excellence in Medical Engineering [WT088641/Z/09/Z]
- NIHR Biomedical Research Centre at Guy's and St. Thomas' NHS Foundation Trust
- KCL
- British Heart Foundation [NH/11/5/29058] Funding Source: researchfish
- Engineering and Physical Sciences Research Council [EP/H046410/1, EP/K030310/1] Funding Source: researchfish
- EPSRC [EP/H046410/1, EP/K030310/1] Funding Source: UKRI
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Advances in medical imaging and image processing are paving the way for personalised cardiac biomechanical modelling. Models provide the capacity to relate kinematics to dynamics and-through patient-specific modelling-derived material parameters to underlying cardiac muscle pathologies. However, for clinical utility to be achieved, model-based analyses mandate robust model selection and parameterisation. In this paper, we introduce a patient-specific biomechanical model for the left ventricle aiming to balance model fidelity with parameter identifiability. Using non-invasive data and common clinical surrogates, we illustrate unique identifiability of passive and active parameters over the full cardiac cycle. Identifiability and accuracy of the estimates in the presence of controlled noise are verified with a number of in silico datasets. Unique parametrisation is then obtained for three datasets acquired in vivo. The model predictions show good agreement with the data extracted from the images providing a pipeline for personalised biomechanical analysis.
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