Key result
A novel framework combining clinical MRI, a biomechanical heart model, and data assimilation successfully estimated patient-specific indices of cardiac function such as local myocardium contractility.
A novel computational framework combining MRI data with a biomechanical heart model shows promise for estimating patient-specific local myocardium contractility.
In this article, we present a framework to estimate cardiac function parameters like local myocardium contractility using clinical MRI, a heart model and data assimilation. First, we build a generic anatomical model of the ventricles including muscle fibre orientations and anatomical subdivisions. Then, this model is deformed to fit a segmented MRI, using an affine registration method and a local deformable biomechanical model. An electromechanical model of the heart can be simulated on this mesh. Data assimilation makes it possible to estimate local contractility from given displacements. Presented results on simulated data and adjustment to clinical data are very promising. Current work on model calibration and estimation of patient parameters open up possibilities for clinical application of this framework.
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Sermesant et al. (2005) studied Cardiac function estimation. Data assimilation framework with MRI and biomechanical heart model was evaluated on Estimation of local contractility. A novel framework combining clinical MRI, a biomechanical heart model, and data assimilation successfully estimated patient-specific indices of cardiac function such as local myocardium contractility.
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