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

Abstract

The last decade has witnessed major progress in the field of minimally invasive and robotic-assisted surgeries. Needle insertion, a minimally invasive technique, has proven its efficacy in procedures such as brachytherapy, ablation, drug delivery, and biopsy. Manual needle steering inside tissue is a challenging task due to complex needle-tissue interactions, needle and tissue movement, lack of actuation and control, as well as poor sensing and visualization. Recently, active tendon-driven notched needles, and robotic manipulation systems have been proposed to assist surgeons to guide the needles in desired trajectories toward target positions. This work introduces a new deflection model for the active tendon-driven notched needle steering inside soft tissue for intention to use in model-based robotic control. The model is developed to predict needle deflection in a single-layer tissue. To validate the proposed deflection model, five sets of needle insertion experiments with a bevel-tipped active needle into single-layer phantom tissues were performed. A real-time robot-assisted ultrasound tracking method was used to track the needle tip during needle insertion. It was shown that the model predicts needle deflection with an average error of 0.58 ± 0.14 mm for the bevel-tipped active needle insertion into a single-layer phantom tissue.