Online parameter estimation for surgical needle steering model

Authors:
Kai Guo Yan;Tarun Podder;Di Xiao;Yan Yu;Tien-I Liu;Keck Voon Ling;Wan Sing Ng
Affiliations:
Schools of MAE, Nanyang Technological University, Singapore;Department of Radiation Oncology, University of Rochester, NY;Schools of MAE, Nanyang Technological University, Singapore;Department of Radiation Oncology, University of Rochester, NY;Computer Integrated Manufacturing Lab, California State University, Sacramento, California;Schools of EEE, Nanyang Technological University, Singapore;Schools of MAE, Nanyang Technological University, Singapore
Venue:
MICCAI'06 Proceedings of the 9th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
Year:
2006

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Abstract

Estimation of the system parameters, given noisy input/output data, is a major field in control and signal processing. Many different estimation methods have been proposed in recent years. Among various methods, Extended Kalman Filtering (EKF) is very useful for estimating the parameters of a nonlinear and time-varying system. Moreover, it can remove the effects of noises to achieve significantly improved results. Our task here is to estimate the coefficients in a spring-beam-damper needle steering model. This kind of spring-damper model has been adopted by many researchers in studying the tissue deformation. One difficulty in using such model is to estimate the spring and damper coefficients. Here, we proposed an online parameter estimator using EKF to solve this problem. The detailed design is presented in this paper. Computer simulations and physical experiments have revealed that the simulator can estimate the parameters accurately with fast convergent speed and improve the model efficacy.