A compact dynamic force model for needle-tissue interaction.

Area of Science:

• Robotics and Control Systems
• Biomedical Engineering
• Soft Tissue Mechanics

Background:

• Accurate modeling of surgical needle-tissue interaction is crucial for robotic surgery and minimally invasive procedures.
• Existing models may not fully capture the complex forces encountered during needle insertion, including puncture, cutting, and friction.
• Understanding these forces is essential for developing safer and more effective surgical tools and techniques.

Purpose of the Study:

• To develop and validate a nonlinear dynamic model for surgical needle-tissue interaction forces.
• To implement an estimation algorithm for identifying model parameters using experimental data.
• To characterize the interaction forces in the force domain for improved surgical simulation and control.

Main Methods:

• A modified LuGre model was employed to capture nonlinear dynamics of needle-tissue interaction.
• An extended Kalman filtering (EKF) based estimation algorithm was developed for parameter identification.
• Experiments were conducted on an artificial phantom to collect axial force data.
• Model parameters were adapted by comparing measured and expected axial forces.

Main Results:

• The modified LuGre model successfully characterized needle-tissue interaction forces, including puncture, cutting, and friction.
• The EKF-based algorithm effectively identified model parameters, enabling mathematical characterization in the force domain.
• Experimental validation on an artificial phantom demonstrated the model's performance in representing actual interaction.

Conclusions:

• The proposed model provides a robust framework for understanding and quantifying surgical needle-tissue interaction forces.
• The EKF approach offers an effective method for real-time parameter estimation and model adaptation.
• This research contributes to the advancement of surgical robotics and simulation by providing a more accurate force model.