Beating heart motion prediction using iterative optimal sine filtering

Abstract

A novel motion prediction algorithm is proposed to robustly track heart beat in minimally invasive surgery. To model the movement of Points of Interest (POI) on heart tissue, the Dual Time-Varying Fourier Series (DTVFS) is employed. The Fourier coefficients and the frequencies of the DTVFS model are estimated separately using the dual Kalman filtering. An iterative optimal sine filtering algorithm is developed, which can accurately measure the instantaneous frequencies of breathing circle and heart beating from the motion curves of the POI. The proposed method is verified on the simulated dataset and the real-measured datasets captured by the daVinci surgical system.