Simultaneous state and parameter estimation for physics-based tracking of heart surface motion

Most existing approaches for tracking of the beating heart motion assume known cardiac kinematics and material parameters. However, these assumptions are not realistic for application in beating heart surgery. In this paper, a novel probabilistic tracking approach based on a physical model of the heart surface is presented. In contrast to existing approaches, the physical information about heart kinematics and material properties is incorporated and considered in an estimation of the heart behavior. An additional advantage is that the time-dependencies and uncertainties of the heart parameters are efficiently handled by exploiting simultaneous state and parameter estimation. Furthermore, by decomposing the state into linear and nonlinear substructures, the computational complexity of the estimation problem is reduced. The experimental results demonstrate the high performance of the method proposed in this paper. The solution of the parameter identification problem allows a personalized physical model and opens up possibilities to apply the physics-based tracking of the heart surface motion in a clinical environment.