Cardiac motion and material properties analysis using data confidence weighted extended Kalman filter framework

A biomechanical model constrained stochastic finite element framework has been developed to jointly estimate myocardium kinematics and material parameters from medical image sequence. In an extended Kalman filter formulation, we have observed that the augmented state error covariance matrix must be carefully chosen in order to avoid divergence. In this paper, we incorporate confidence measures of the input imaging and imaging-derived data into the initialization of the state error covariance matrix. These confidence measures come from the shape-matching process of boundary points and from the local phase coherence of the magnetic resonance velocity images. Experiments with two types of imaging inputs have shown vastly improved filtering efficiency and physiologically meaningful results.