Model-Based Robust Pose Estimation for a Multi-Segment, Programmable Bevel-Tip Steerable Needle

Abstract

Bevel-tip steerable needles for percutaneous intervention are prone to torsion determined by the interaction forces with the human tissue. If disregarded, torsion can affect the insertion accuracy inducing a change in the needle tip orientation, which is generally undetectable by tracking devices because of the small diameter of the needle. This letter presents a method for estimating the tip pose (i.e. position and orientation) of a programmable bevel-tip needle using a 2-D kinematic based Extended Kalman Filter (EKF), where the tip position of the two steering segments is used as input measurement. Simulation trials and experiments in phantom-brain gelatin were performed to prove the performance of the method and mimic real case scenarios. The solution presented shows state-of-the-art performance in needle pose estimation with a bounded positional error of <; 1 mm and orientation error of <; 5°.