Nonlinear cardiac deformation recovery from medical images
To recover physiologically meaningful cardiac deformation from medical images, realistic physiological models are essential to constrain the recovery process, and a statistical filtering framework is required to couple the models and images according to their respective uncertainties. As realistic cardiac models are usually nonlinear, existing cardiac deformation recovery frameworks either ignore the statistical filtering part, or linearize the model and apply linear filtering techniques such as the extended Kalman filtering. This reduces the physiological plausibility and statistical optimality of the recovery results. In this paper, we propose a nonlinear cardiac deformation recovery framework with unscented Kalman filtering which preserves the intact system nonlinearity. Experiments were done on both synthetic data and magnetic resonance images to show the benefits and clinical relevance of our framework.