Finite deformation guided nonlinear filtering for multiframe cardiac motion analysis

Abstract

In order to obtain sensible estimates of myocardial kinematics based on biomechanics constraints, one must adopt appropriate material, deformation, and temporal models. Earlier efforts, although not concurrently adopted within the same framework, have shown that it is essential to carefully consider the fibrous structure of the myocardium, the large geometric deformation of the cardiac wall movement, the multiframe observations over the cardiac cycle, and the uncertainties in the system modeling and data measurements. With the meshfree particle method providing the platform to enforce the anisotropic material property derived from the myofiber architecture, we present the first effort to perform multiframe cardiac motion analysis under finite deformation conditions, posed as a nonlinear statistical filtering process. Total Lagrangian (TL) formulation is adopted to establish the myocardial system dynamics under finite deformation, which is then used to perform nonlinear state space prediction (of the tissue displacement and velocity) at each time frame, using the Newton-Raphson iteration scheme. The system matrices of the state space equation are then derived, and the optimal estimation of the kinematic state is achieved through TL-updated recursive filtering. Results from synthetic data with ground truth and canine cardiac image sequence are presented. © Springer-Verlag Berlin Heidelberg 2004.