Needle Steering Research Articles

Hyper- and viscoelastic modeling of needle and brain tissue interaction.

Deep needle insertion into brain is important for both diagnostic and therapeutic clinical interventions. We have developed an automated system for robotically steering flexible needles within the brain to improve targeting accuracy. In this work, we have developed a finite element needle-tissue interaction model that allows for the investigation of safe parameters for needle steering. The tissue model implemented contains both hyperelastic and viscoelastic properties to simulate the instantaneous and time-dependent responses of brain tissue. Several needle models were developed with varying parameters to study the effects of the parameters on tissue stress, strain and strain rate during needle insertion and rotation. The parameters varied include needle radius, bevel angle, bevel tip fillet radius, insertion speed, and rotation speed. The results will guide the design of safe needle tips and control systems for intracerebral needle steering.

Needle geometry, target migration and substrate interactions in high resolution.

Recent investigations considering flexible, steer-able needles for minimally invasive surgery have shown the significance of needle shape in determining the needle-tissue interactions leading to the access of targets. Digital Image Correlation has enabled internal deformation and strain caused by needle insertions to be seen in a soft tissue phantom at high resolution for the first time. Here, the impact of tip design on strains and displacements of material around the insertion axis is presented using Digital Image Correlation in a stable, plane-strain configuration. Insight into the shape of needles to minimise tissue trauma and generate interactions that would enable optimal steering conditions is provided. Needle tips with an included bevel angle up to 40° result in asymmetric displacement of the surrounding tissue phantom. Increasing the included tip angle to 60° results in more predictable displacement and strains that may enhance steering forces with little negative impact on the phantom.

Three-Dimensional Needle Shape Reconstruction Using an Array of Fiber Bragg Grating Sensors

We present a prototype of a flexible nitinol needle (φ 1.0 mm and length 172 mm) integrated with an array of 12 Fiber Bragg Grating (FBG) sensors. These sensors measure the axial strain, which enables the computation of the needle curvature. We reconstruct the three-dimensional (3-D) needle shape from the curvature. Experiments are performed where the needle is deflected in free space. The maximum errors between the experiments and beam theory-based model are 0.20 mm (in-plane deflection with single bend), 0.51 mm (in-plane deflection with double bend), and 1.66 mm (out-of-plane). We also describe kinematics-based and mechanics-based models for predicting the 3-D needle shape during insertion into soft tissue. We perform experiments where the needle is inserted into a soft-tissue simulant, and the 3-D needle shape is reconstructed using the FBG sensors. We compare the reconstructed needle shape to deflection obtained from camera images and our models. The maximum error between the experiments and the camera images is 0.74 mm. The maximum errors between the kinematics-based and mechanics-based models and the camera images are 3.77 mm and 2.20 mm, respectively. This study demonstrates that deflection models and needles integrated with FBG sensors have the potential to be used in combination with clinical imaging modalities in order to enable accurate needle steering.

Estimation of flexible needle deflection in layered soft tissues with different elastic moduli.

The estimation of needle deflection when the needle interacts with biological tissue is challenging in needle steering. Most previous studies have used homogeneous tissue models to estimate the deflection of a needle. However, biological tissue typically has a layered structure with variable mechanical properties and geometric features. In this study, we propose a needle deflection model with a double-layered elastic medium. Each layer possesses distinct mechanical properties, which result in inconsistent degrees of friction force on the needle surface, needle-cutting force, and forces caused by the stiffness of the elastic medium. The model uses the Rayleigh-Ritz method to analytically estimate the needle deflection. To validate the model, needle steering experiments were performed using double-layered tissue phantoms and porcine tissues. The experimental results revealed that changes in reaction force at the needle base occurred when the needle passed through the boundary between the two layers. The mean absolute error between the estimated and measured needle tip trajectory was 0.88±0.30mm for the double-layered tissue phantom and 1.85±0.73mm for the porcine tissue. These results are comparable with previous studies of homogeneous tissue. The proposed model could improve needle steering in biological tissues, which consist of multiple layers.

3-D ultrasound-guided robotic needle steering in biological tissue.

Robotic needle steering systems have the potential to greatly improve medical interventions, but they require new methods for medical image guidance. Three-dimensional (3-D) ultrasound is a widely available, low-cost imaging modality that may be used to provide real-time feedback to needle steering robots. Unfortunately, the poor visibility of steerable needles in standard grayscale ultrasound makes automatic segmentation of the needles impractical. A new imaging approach is proposed, in which high-frequency vibration of a steerable needle makes it visible in ultrasound Doppler images. Experiments demonstrate that segmentation from this Doppler data is accurate to within 1-2 mm. An image-guided control algorithm that incorporates the segmentation data as feedback is also described. In experimental tests in ex vivo bovine liver tissue, a robotic needle steering system implementing this control scheme was able to consistently steer a needle tip to a simulated target with an average error of 1.57 mm. Implementation of 3-D ultrasound-guided needle steering in biological tissue represents a significant step toward the clinical application of robotic needle steering.

Needle path planning and steering in a three-dimensional non-static environment using two-dimensional ultrasound images

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).

Motion planning with sequential convex optimization and convex collision checking

We present a new optimization-based approach for robotic motion planning among obstacles. Like CHOMP (Covariant Hamiltonian Optimization for Motion Planning), our algorithm can be used to find collision-free trajectories from naïve, straight-line initializations that might be in collision. At the core of our approach are (a) a sequential convex optimization procedure, which penalizes collisions with a hinge loss and increases the penalty coefficients in an outer loop as necessary, and (b) an efficient formulation of the no-collisions constraint that directly considers continuous-time safety Our algorithm is implemented in a software package called TrajOpt. We report results from a series of experiments comparing TrajOpt with CHOMP and randomized planners from OMPL, with regard to planning time and path quality. We consider motion planning for 7 DOF robot arms, 18 DOF full-body robots, statically stable walking motion for the 34 DOF Atlas humanoid robot, and physical experiments with the 18 DOF PR2. We also apply TrajOpt to plan curvature-constrained steerable needle trajectories in the SE(3) configuration space and multiple non-intersecting curved channels within 3D-printed implants for intracavitary brachytherapy. Details, videos, and source code are freely available at: http://rll.berkeley.edu/trajopt/ijrr.

Torsional dynamics of steerable needles: modeling and fluoroscopic guidance.

Needle insertions underlie a diversity of medical interventions. Steerable needles provide a means by which to enhance existing needle-based interventions and facilitate new ones. Tip-steerable needles follow a curved path and can be steered by twisting the needle base during insertion, but this twisting excites torsional dynamics that introduce a discrepancy between the base and tip twist angles. Here, we model the torsional dynamics of a flexible rod-such as a tip-steerable needle-during subsurface insertion and develop a new controller based on the model. The torsional model incorporates time-varying mode shapes to capture the changing boundary conditions inherent during insertion. Numerical simulations and physical experiments using two distinct setups-stereo camera feedback in semitransparent artificial tissue and feedback control with real-time X-ray imaging in optically opaque artificial tissue-demonstrate the need to account for torsional dynamics in control of the needle tip.

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.

Dynamic Path Planning for Inserting a Steerable Needle Into a Soft Tissue

Steerable bevel-tip needles are widely used in modern, minimally invasive percutaneous procedures to reach specific areas inside the body. In this paper, we propose an optimized path planner for manipulating such steerable needles, which can generate the shortest path from the starting position to the target position with the least number of rotation times. The shortest traveling path produces less damage to the body, thus shortening the recovery period. We first investigate the insertion problem in a nondeformable environment, which is termed as the static environment in this paper. As the needle is flexible, the moving path is a curve. We propose to regulate the curved path within two parallel lines and then determine the optimal distance between the two parallel lines such that the generated moving path of the needle has the shortest length with the least number of needle rotations. We then investigate the insertion problem in a deformable environment, which is termed as the dynamic environment. Taking deformation and nonhomogenous properties of soft tissue into account, the target position and the radius of the curve path vary as the needle is inserted. By using a mass-spring model to formulate the deformable environment and a vision system to measure the time-varying radius of the curve, we propose a dynamic path planner that replans the path to adapt to the change of the target position and the curve radius. Simulations and experiments are performed to demonstrate the effectiveness of the proposed approach.

Dynamical modeling and controllability analysis of a flexible needle in soft tissue

This paper is concerned with deriving a dynamic model of a moderately flexible needle inserted into soft tissue, where the model's output is the needle deflection. The main advantages of the proposed dynamic modeling approach are that the presented model structure involves parameters that are all measurable or identifiable by simple experiments and that it considers the same inputs that are currently used in the clinical practice of manual needle insertion. Conventional manual needle insertion suffers from the fact that flexible needles bend during insertion and their trajectories often vary from those planned, resulting in positioning errors. Enhancement of needle insertion accuracy via robot-assisted needle steering has received significant attention in the past decade. A common assumption in previous research has been that the needle behavior during insertion can be adequately described by static models relating the needle's forces and torques to its deflection. For closed-loop control purposes, however, a dynamic model of the flexible needle in soft tissue is desired. In this paper, we propose a Lagrangian-based dynamic model for the coupled needle/tissue system, and analyze the response of the dynamic system. Steerability (controllability) analysis is also performed, which is only possible with a dynamic model. The proposed dynamic model can serve as a cornerstone of future research into designing dynamics-based control strategies for closed-loop needle steering in soft tissue aimed at minimizing position error.

Experimental evaluation of ultrasound-guided 3D needle steering in biological tissue

In this paper, we present a system capable of automatically steering bevel tip flexible needles under ultrasound guidance toward stationary and moving targets in gelatin phantoms and biological tissue while avoiding stationary and moving obstacles. We use three-dimensional (3D) ultrasound to track the needle tip during the procedure. Our system uses a fast sampling-based path planner to compute and periodically update a feasible path to the target that avoids obstacles. We then use a novel control algorithm to steer the needle along the path in a manner that reduces the number of needle rotations, thus reducing tissue damage. We present experimental results for needle insertion procedures for both stationary and moving targets and obstacles for up to 90 mm of needle insertion. We obtained a mean targeting error of [Formula: see text] and [Formula: see text] mm in gelatin-based phantom and biological tissue, respectively. The achieved submillimeter accuracy suggests that our approach is sufficient to target the smallest lesions ([Formula: see text] 2 mm) that can be detected using state-of-the-art ultrasound imaging systems.

3D Robust Online Motion Planning for Steerable Needles in Dynamic Workspaces Using Duty-Cycled Rotation

This work presents a closed-loop strategy for 3D online motion planning of beveled steerable needles using duty-cycled rotation. The algorithm first selects an entry point that minimizes a multi-criteria cost function and then combines an RRT-based path planner with an intraoperative replanning algorithm and workspace feedback information to constantly update the needle inputs and adjust the trajectory. Simulations in a workspace based on a typical prostate needle steering scenario show that the algorithm is robust against disturbances and model uncertainties and can provide online trajectories to avoid obstacles even under the presence of physiological motion.

3D steering of a flexible needle by visual servoing.

This paper presents a robotic control method for 3D steering of a beveled-tip flexible needle. The solution is based on a new duty-cycling control strategy that makes possible to control three degrees of freedom of the needle. A visual servoing control scheme using two orthogonal cameras observing a translucent phantom is then proposed to automatically steer a needle toward a 3D target point. Experimental results show a final positioning error of 0.4 mm and demonstrate the feasibility of this promising approach and its robustness to model errors.

Future robotic platforms in urologic surgery

To review recent developments at Vanderbilt University of new robotic technologies and platforms designed for minimally invasive urologic surgery and their design rationale and potential roles in advancing current urologic surgical practice. Emerging robotic platforms are being developed to improve performance of a wider variety of urologic interventions beyond the standard minimally invasive robotic urologic surgeries conducted currently with the da Vinci platform. These newer platforms are designed to incorporate significant advantages of robotics to improve the safety and outcomes of transurethral bladder surgery and surveillance, further decrease the invasiveness of interventions by advancing LESS surgery, and to allow for previously impossible needle access and ablation delivery. Three new robotic surgical technologies that have been developed at Vanderbilt University are reviewed, including a robotic transurethral system to enhance bladder surveillance and transurethral bladder tumor, a purpose-specific robotic system for LESS, and a needle-sized robot that can be used as either a steerable needle or small surgeon-controlled micro-laparoscopic manipulator.

Needle path planning and steering in a three-dimensional non-static environment using two-dimensional ultrasound images

Needle path planning and steering in a three-dimensional non-static environment using two-dimensional ultrasound images

A Combination Method of Artificial Potential Field and Improved Conjugate Gradient for Trajectory Planning for Needle Insertion into Soft Tissue

To reduce postoperative complications and unwanted side-effects, injury to vital tissues and/or nerves should be avoided in minimally invasive surgery. In this paper, a combined trajectory planning algorithm that uses an artificial potential field (APF) method in conjunction with an improved conjugate gradient algorithm (ICGA) is proposed. Fascial tissues create local minima (LM) when the APF method is applied, which may lead to needle oscillation. The proposed method finds sub-goal points using ICGA to escape from LM. Thus, a complete smooth trajectory passing through anatomical obstacles is obtained. Needle guidance and steering can be performed through a unicycle model. The optimal APF function is obtained by adjusting parameter n. When n=6, the distance between LM and the target reached its minimum (1.0198), which shows that Point 6 is closest to the target and n=6 performs higher search efficiency. The experimental simulation shows combination method has higher efficiency (13.45% higher) compared to APF method, which requires just a few tenths of a second on a standard PC.

Real-time 3D curved needle segmentation using combined B-mode and power Doppler ultrasound.

This paper presents a real-time segmentation method for curved needles in biological tissue based on analysis of B-mode and power Doppler images from a tracked 2D ultrasound transducer. Mechanical vibration induced by an external voice coil results in a Doppler response along the needle shaft, which is centered around the needle section in the ultrasound image. First, B-mode image analysis is performed within regions of interest indicated by the Doppler response to create a segmentation of the needle section in the ultrasound image. Next, each needle section is decomposed into a sequence of points and transformed into a global coordinate system using the tracked transducer pose. Finally, the 3D shape is reconstructed from these points. The results of this method differ from manual segmentation by 0.71 ± 0.55 mm in needle tip location and 0.38 ± 0.27 mm along the needle shaft. This method is also fast, taking 5-10 ms to run on a standard PC, and is particularly advantageous in robotic needle steering, which involves thin, curved needles with poor echogenicity.

Experimental evaluation of ultrasound-guided 3D needle steering in biological tissue

Experimental evaluation of ultrasound-guided 3D needle steering in biological tissue

A new geometry-based plan for inserting flexible needles to reach multiple targets

SUMMARYThe tip of a flexible needle with a bevel tip approximately follows a planar arc when it is inserted into soft tissue only with the force applied to the needle along the needle axis. The direction of the arc can be controlled by the rotation input around the needle axis. This flexible and steerable needle has been shown to have a considerable potential in clinical applications due to its maneuverability and steerability. Beyond the needle insertion to a single destination, this paper concerns obtaining needle trajectories that reach multiple targets. Specifically, we propose an algorithm for the insertion of a flexible needle to travel from a single insertion point (i.e. port) to multiple targets. The insertion is motivated by the observation that multiple targets can be reached by the flexible needle through a combination of insertion, partial retraction, turning, and reinsertion of the flexible needle. In this paper we develop an insertion algorithm that minimizes tissue damage during the needle insertion to multiple targets. To this end, a cost function which computes the length of needle trajectory that can be thought of as the tissue damage is defined, and is minimized. Through the minimization, we find the optimal insertion parameters such as the port location, the insertion direction at the port, the targeting order, the turning angles, and the lengths of forward insertions and retractions. To reduce the computation time, we perform workspace analysis for this approach to filter out the no-solution cases. We present numerical examples of the simulated needle insertion for multiple targets with and without obstacles and show the benefit of the proposed method in terms of the tissue damage and the number of skin punctures. Extensions of the proposed approach to more complex cases such as more than three target points and maneuvering around spherical obstacles are also discussed.