Needle insertion procedures are commonly used for diagnostic and therapeutic purposes. In this paper, an imageguided control system is developed to robotically steer flexible needles with an asymmetric tip. Knowledge about needle deflection is required for accurate steering. Two different models to predict needle deflection are presented. The first is a kinematics-based model, and the second model predicts needle deflection that is based on the mechanics of needle-tissue interaction. Both models predict deflection of needles that undergo multiple bends. The maximum targeting errors of the kinematics-based and the mechanics-based models for 110-mm insertion distance using a φ 0.5-mm needle are 0.8 and 1.7 mm, respectively. The kinematics-based model is used in the proposed image-guided control system. The control system accounts for target motion during the insertion procedure by detecting the target position in each image frame. Five experimental cases are presented to validate the real-time control system using both camera and ultrasound images as feedback. The experimental results show that the targeting errors of camera and ultrasound image-guided steering toward a moving target are 0.35 and 0.42 mm, respectively. The targeting accuracy of the algorithm is sufficient to reach the smallest lesions (φ 2 mm) that can be detected using the state-of-the-art ultrasound imaging systems.

Needle insertion is one of the most commonly performed minimally invasive procedures. Visualization of the needle during insertion is key for either successful diagnosis or therapy. This work presents the real-time three-dimensional tracking of flexible needles during insertion into a soft-tissue simulant using a two-dimensional (2D) ultrasound transducer. The transducer is placed perpendicular to the needle tip to measure its position. During insertion the transducer is robotically repositioned to track the needle tip. Positioning of the transducer is accomplished by a compensator, that uses the needle insertion velocity corrected by needle tip velocities to determine out-of-plane motion. Experiments are performed to validate the needle tip pose during tracking. The maximum mean errors in needle tip position along the x-, y-and z-axes are 0.64 mm, 0.25 mm and 0.27 mm, respectively. The error in tip orientations (θ-about the y-axis and φ-about the z-axis) are 2.68 • and 2.83 • , respectively. This study demonstrates the ability to compute the needle tip pose using a 2D ultrasound transducer. The tip pose can be used to robotically steer needles, and thereby improve accuracy of medical procedures.

Needle insertion is commonly performed in minimally invasive medical procedures such as biopsy and radiation cancer treatment. During such procedures, accurate needle tip placement is critical for correct diagnosis or successful treatment. Accurate placement of the needle tip inside tissue is challenging, especially when the target moves and anatomical obstacles must be avoided. We develop a needle steering system capable of autonomously and accurately guiding a steerable needle using two-dimensional (2D) ultrasound images. The needle is steered to a moving target while avoiding moving obstacles in a three-dimensional (3D) non-static environment. Using a 2D ultrasound imaging device, our system accurately tracks the needle tip motion in 3D space in order to estimate the tip pose. The needle tip pose is used by a rapidly exploring random tree-based motion planner to compute a feasible needle path to the target. The motion planner is sufficiently fast such that replanning can be performed repeatedly in a closed-loop manner. This enables the system to correct for perturbations in needle motion, and movement in obstacle and target locations. Our needle steering experiments in a soft-tissue phantom achieves maximum targeting errors of 0.86 ± 0.35 mm (without obstacles) and 2.16 ± 0.88 mm (with a moving obstacle).

Needle insertion procedures are commonly used for surgical interventions. In this paper, we develop a threedimensional (3D) closed-loop control algorithm to robotically steer flexible needles with an asymmetric tip towards a target in a soft-tissue phantom. Twelve Fiber Bragg Grating (FBG) sensors are embedded on the needle shaft. FBG sensors measure the strain applied on the needle during insertion. A method is developed to reconstruct the needle shape using the strain data obtained from the FBG sensors. Four experimental cases are conducted to validate the reconstruction method (singlebend, double-bend, 3D double-bend and drilling insertions). In the experiments, the needle is inserted 120 mm into a softtissue phantom. Camera images are used as a reference for the reconstruction experiments. The results show that the mean needle tip accuracy of the reconstruction method is 1.8 mm. The reconstructed needle shape is used as feedback for the steering algorithm. The steering algorithm estimates the region that the needle can reach during insertion, and controls the needle to keep the target in this region. Steering experiments are performed for 110 mm insertion, and the mean targeting accuracy is 1.3 mm. The results demonstrate the capability of using FBG sensors to robotically steer needles.

In this paper, we present a system capable of automatically steering a bevel-tipped flexible needle under ultrasound guidance toward a physical target while avoiding a physical obstacle embedded in gelatin phantoms and biological tissue with curved surfaces. An ultrasound pre-operative scan is performed for three-dimensional (3D) target localization and shape reconstruction. A controller based on implicit force control is developed to align the transducer with curved surfaces to assure the maximum contact area, and thus obtain an image of sufficient quality. We experimentally investigate the effect of needle insertion system parameters such as insertion speed, needle diameter and bevel angle on target motion to adjust the parameters that minimize the target motion during insertion. A fast sampling-based path planner is used to compute and periodically update a feasible path to the target that avoids obstacles. We present experimental results for target reconstruction and needle insertion procedures in gelatin-based phantoms and biological tissue. Mean targeting errors of 1.46 ± 0.37 mm, 1.29 ± 0.29 mm and 1.82 ± 0.58 mm are obtained for phantoms with inclined, curved and combined (inclined and curved) surfaces, respectively, for insertion distance of 86–103 mm. The achieved targeting errors suggest that our approach is sufficient for targeting lesions of 3 mm radius that can be detected using clinical ultrasound imaging systems.