Needle Steering in Biological Tissue using Ultrasound-based Online Curvature Estimation

Percutaneous needle insertions are commonly performed for diagnostic and therapeutic purposes. Accurate placement of the needle tip is important to the success of many needle procedures. The current needle steering systems depend on needle-tissue-specific data, such as maximum curvature, that is unavailable prior to an interventional procedure. In this paper, we present a novel three-dimensional adaptive steering method for flexible bevel-tipped needles that is capable of performing accurate tip placement without previous knowledge about needle curvature. The method steers the needle by integrating duty-cycled needle steering, online curvature estimation, ultrasound-based needle tracking, and sampling-based motion planning. The needle curvature estimation is performed online and used to adapt the path and duty cycling. We evaluated the method using experiments in a homogenous gelatin phantom, a two-layer gelatin phantom, and a biological tissue phantom composed of a gelatin layer and in vitro chicken tissue. In all experiments, virtual obstacles and targets move in order to represent the disturbances that might occur due to tissue deformation and physiological processes. The average targeting error using our new adaptive method is 40% lower than using the conventional non-adaptive duty-cycled needle steering method.