Reduced-Order Extended Kalman Filter for Deformable Tissue Simulation

Modelling of soft tissue deformation is a key issue in surgical simulation. Despite extensive research studies on this issue, accurate modelling of soft tissue deformation in run time still remains challenging. This paper proposes a new reduced-order nonlinear Kalman filter to emulate nonlinear behaviors of biological deformable tissues. This approach defines the deformable modelling problem as a reduced-order filtering problem to accurately calculate soft tissue deformation in real time. Soft tissue deformation is discretized in space using nonlinear finite element method based on hyperelasticity and further formulated as a nonlinear state-space equation for filtering estimation. Subsequently, the order of this nonlinear state-space equation is reduced using proper orthogonal decomposition to reduce the computational cost. Upon this reduced-order state-space equation, an extended Kalman filter is constructed to online calculate nonlinear behaviors of tissue physical deformation. Simulation results and comparison analysis prove the effectiveness of the suggested method for accurate simulation of tissue physical deformation in real time.

Automated summary

The paper introduces a new reduced-order nonlinear Kalman filter to emulate nonlinear behaviors of biological deformable tissues for accurate simulation of tissue physical deformation in real time. The approach reduces the order of the nonlinear state-space equation to decrease computational cost, constructing an extended Kalman filter to calculate tissue physical deformation behaviors online. Simulation results and comparison analysis verify the effectiveness of the proposed method.