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Online parameter estimation for surgical needle steering model.

Kai Guo Yan¹, Tarun Podder, Di Xiao

¹Schools of MAE, Nanyang Technological University, Singapore.

Medical Image Computing and Computer-Assisted Intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention

|March 16, 2007

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This study introduces an online parameter estimator using Extended Kalman Filtering (EKF) to accurately determine spring and damper coefficients in needle steering models, enhancing tissue deformation analysis.

Area of Science:

Control Engineering
Signal Processing
Biomedical Engineering

Background:

System parameter estimation from noisy data is crucial in control and signal processing.
Extended Kalman Filtering (EKF) is effective for nonlinear, time-varying systems and noise reduction.
Estimating spring and damper coefficients in spring-damper models for tissue deformation is challenging.

Purpose of the Study:

To develop an online parameter estimator for a spring-beam-damper needle steering model.
To accurately estimate the spring and damper coefficients of the needle steering model.
To improve the efficacy of tissue deformation models.

Main Methods:

Utilized Extended Kalman Filtering (EKF) for online parameter estimation.
Designed a specific estimator for spring and damper coefficients.

Related Experiment Videos

Validated the method through computer simulations and physical experiments.

Main Results:

The proposed EKF-based estimator accurately determined the model parameters.
The estimation process demonstrated a fast convergent speed.
The model efficacy was significantly improved by the accurate parameter estimation.

Conclusions:

The online parameter estimator using EKF is effective for needle steering models.
Accurate estimation of spring and damper coefficients enhances tissue deformation analysis.
The method offers a robust solution for parameter identification in complex systems.