Computational complexity reduction via mode superposition: Application to biomechanics-based nonlinear cardiac deformation recovery

To systematically couple images and physiological models according to their respective merits, state-space filtering frameworks have been proposed for cardiac deformation recovery with promising results. Nevertheless, as thousands of forward simulations are required in every filtering step, the computational complexity is too high to be practical. To reduce the computational complexity without a significant loss of accuracy, we have adopted the mode superposition approach which transforms the cardiac system dynamics to a mathematically equivalent space of much lower dimensions. With the corresponding filtering procedures and components proposed, nonlinear cardiac deformation recovery can be performed in the transformed space with largely reduced computational complexity. Experiments were performed on synthetic data to evaluate the computational complexity and accuracy, and on human data for the clinical relevance.