FBG-based Shape-Sensing to Enable Lateral Deflection Methods of Autonomous Needle Insertion.

In diagnosing and treating prostate cancer the flexible bevel tip needle insertion surgical technique is commonly used. Bevel tip needles experience asymmetric loading on the needle's tip, inducing natural bending of the needle and enabling control mechanisms for precise placement of the needle during surgery. Several methods leverage the needles natural bending to provide autonomous control of needle insertion for accurate needle placement in an effort to reduce excess tissue damage and improve patient outcomes from needle insertion intraventions. Moreover, control methods using lateral deflection of the needle intra-operatively to steer the needle during insertion have been studied and have shown promising results. Thus, to enable these autonomous control methods, real-time, intra-operative shape-sensing feedback is pivotal for optimal performance of the needle insertion control. This work presents an extension of our proven Lie-group theoretic shape-sensing model to handle lateral deflection of the needle during needle insertion and validate this extension with robotic needle insertions in phantom tissue using stereo vision as a ground truth. Furthermore, the system configuration for real-time shape-sensing is implemented using ROS 2, demonstrating average feedback frequency of 15 ± 8 Hz. Average needle shape errors realized from this extension under 1 mm, validating the shape-sensing models' extension.