Needle-tissue interaction model based needle path planning method.

Area of Science:

• Medical Robotics
• Biomedical Engineering
• Surgical Planning

Background:

• Needle insertion accuracy is challenged by tissue deflection and target movement.
• Existing planning methods struggle with patient-specific variations, leading to increased targeting errors.

Purpose of the Study:

• To propose a patient-specific needle path planning method incorporating a needle-tissue interaction model.
• To enhance targeting accuracy by addressing patient-specific differences and real-time tissue deformation.

Main Methods:

• Developed a 3D needle-tissue interaction deformation model (full computation model) using local constraints.
• Utilized Kriging-based prediction for interaction force, target movement, and needle deflection.
• Employed Rapidly-exploring Random Trees (RRT) for initial path planning, followed by iterative learning control for error correction.
• Introduced an intraoperative parameter identification algorithm for patient-specific calibration.

Main Results:

• Simulations confirmed the full computation model's ability to simulate needle-tissue interaction and enable deformation-aware path planning.
• Experimental validation showed good agreement between predicted and actual tissue deformation and needle deflection.
• The proposed method reduced maximum targeting error from 3.89 mm to under 1 mm in phantom experiments.

Conclusions:

• The full computation model-based needle path planning is experimentally validated as effective.
• Incorporating predicted tissue deformation significantly improves planning accuracy.
• The method achieves desired targeting accuracy by accounting for patient-specific factors.