A Model to Predict Deflection of an Active Tendon-Driven Notched Needle Inside Soft Tissue

Area of Science:

• Robotics
• Medical Engineering
• Surgical Technology

Background:

• Minimally invasive and robotic-assisted surgeries have advanced significantly.
• Needle insertion is crucial for procedures like brachytherapy, ablation, drug delivery, and biopsy.
• Manual needle steering is challenging due to tissue interaction, movement, and limited control/visualization.

Purpose of the Study:

• Introduce a novel deflection model for active tendon-driven notched needle steering in soft tissue.
• Enable model-based robotic control for precise needle guidance.
• Predict needle deflection within single-layer tissue models.

Main Methods:

• Developed a new deflection model for active tendon-driven notched needles.
• Conducted five needle insertion experiments using a bevel-tipped active needle into phantom tissues.
• Employed real-time robot-assisted ultrasound tracking to monitor needle tip trajectory.

Main Results:

• The proposed deflection model was validated through experimental insertion.
• Needle deflection was predicted with an average error of 0.58 ± 0.14 mm.
• The model demonstrated accuracy for bevel-tipped active needle insertion in single-layer phantom tissue.

Conclusions:

• The developed deflection model shows promise for enhancing robotic control in needle insertion procedures.
• Accurate prediction of needle deflection is vital for improving surgical precision.
• This model contributes to the advancement of robotic-assisted minimally invasive surgery.