Bevel-tip Needle Research Articles

A method for predicting needle insertion deflection in soft tissue based on cutting force identification

The deflection modeling during the insertion of bevel-tipped flexible needles into soft tissues is crucial for robot-assisted flexible needle insertion into specific target locations within the human body during percutaneous biopsy surgery. This paper proposes a mechanical model based on cutting force identification to predict the deflection of flexible needles in soft tissues. Unlike other models, this method does not require measuring Young’s modulus ( E ) and Poisson’s ratio ( ν ) of tissues, which require complex hardware to obtain. In the model, the needle puncture process is discretized into a series of uniform-depth puncture steps. The needle is simplified as a cantilever beam supported by a series of virtual springs, and the influence of tissue stiffness on needle deformation is represented by the spring stiffness coefficient of the virtual spring. By theoretical modeling and experimental parameter identification of cutting force, the spring stiffness coefficients are obtained, thereby modeling the deflection of the needle. To verify the accuracy of the proposed model, the predicted model results were compared with the deflection of the puncture experiment in polyvinyl alcohol (PVA) gel samples, and the average maximum error range predicted by the model was between 0.606 ± 0.167 mm and 1.005 ± 0.174 mm, which showed that the model can successfully predict the deflection of the needle. This work will contribute to the design of automatic control strategies for needles.

Bevel-Tip Needle Deflection Modeling, Simulation, and Validation in Multi-Layer Tissues.

Percutaneous needle insertions are commonly performed for diagnostic and therapeutic purposes as an effective alternative to more invasive surgical procedures. However, the outcome of needle-based approaches relies heavily on the accuracy of needle placement, which remains a challenge even with robot assistance and medical imaging guidance due to needle deflection caused by contact with soft tissues. In this paper, we present a novel mechanics-based 2D bevel-tip needle model that can account for the effect of nonlinear strain-dependent behavior of biological soft tissues under compression. Real-time finite element simulation allows multiple control inputs along the length of the needle with full three-degree-of-freedom (DOF) planar needle motions. Cross-validation studies using custom-designed multi-layer tissue phantoms as well as heterogeneous chicken breast tissues result in less than 1mm in-plane errors for insertions reaching depths of up to 61 mm, demonstrating the validity and generalizability of the proposed method.

A mechanics-based model for predicting flexible needle bending with large curvature in soft tissue

Percutaneous insertion is one of the most common minimally invasive procedures. Compared with traditional straight rigid needles, bevel-tipped flexible needle can generate curved trajectories to avoid obstacles and sensitive organs. However, the nonlinear large deflection problem challenges the bending prediction of the needle, which dramatically influences the surgical success rate. This paper analyzed the mechanism of needle-tissue interaction, and established a mechanics-based model of the needle bending during an insertion. And then, a discretization of the bending model was adopted to accurately predict the large bending of the needle in soft tissue. Insertion experiments were conducted to validate the bending prediction model. The results showed that the large needle bending was predicted with the mean/RMSE/maximumu error of 0.42 mm / 0.26 mm / 1.08 mm, which was clinically acceptable. This proved the rationality and accuracy of the proposed model.

A Comparison between Kinematic Models for Robotic Needle Insertion with Application into Transperineal Prostate Biopsy

Transperineal prostate biopsy is the most reliable technique for detecting prostate cancer, and robot-assisted needle insertion has the potential to improve the accuracy of this procedure. Modeling the interaction between a bevel-tip needle and the tissue, considering tissue heterogeneity, needle bending, and tissue/organ deformation and movement is a required step to enable robotic needle insertion. Even if several models exist, they have never been compared on experimental grounds. Based on this motivation, this paper proposes an experimental comparison for kinematic models of needle insertion, considering different needle insertion speeds and different degrees of tissue stiffness. The experimental comparison considers automated insertions of needles into transparent silicone phantoms under stereo-image guidance. The comparison evaluates the accuracy of existing models in predicting needle deformation.

Optical Fiber-Based Needle Shape Sensing in Real Tissue: Single Core vs. Multicore Approaches.

Flexible needle insertion procedures are common in minimally-invasive surgeries for diagnosing and treating prostate cancer. Bevel-tip needles provide physicians the capability to steer the needle during long insertions to avoid vital anatomical structures in the patient and reduce post-operative patient discomfort. To provide needle placement feedback to the physician, sensors are embedded into needles for determining the real-time 3D shape of the needle during operation without needing to visualize the needle intra-operatively. Through expansive research in fiber optics, a plethora of bio-compatible, MRI-compatible, optical shape-sensors have been developed to provide real-time shape feedback, such as single-core and multicore fiber Bragg gratings. In this paper, we directly compare single-core fiber-based and multicore fiber-based needle shape-sensing through similarly constructed, four-active area sensorized bevel-tip needles inserted into phantom and ex-vivo tissue on the same experimental platform. In this work, we found that for shape-sensing in phantom tissue, the two needles performed identically with a p-value of 0.164 > 0.05, but in ex-vivo real tissue, the single-core fiber sensorized needle significantly outperformed the multicore fiber configuration with a p-value of 0.0005 < 0.05. This paper also presents the experimental platform and method for directly comparing these optical shape sensors for the needle shape-sensing task, as well as provides direction, insight and required considerations for future work in constructively optimizing sensorized needles.

A Passive Model-Based Error Compensation Approach for Bevel Tip Needle Deflection

The interaction between an asymmetric (bevel) tipped needle and the surrounding tissue in image-guided percutaneous interventions, such as in prostate needle biopsy, can result in the deflection of the needle tip and cause significant targeting error. Researchers have extensively investigated different aspects of the needle-tissue interaction and proposed various solutions to mitigate this issue. While there are several promising approaches, most require complex software or hardware which makes them difficult to be deployed for clinical use. In this paper, we present a predictive model-based approach to passively compensate for undesired deflection of the needle tip prior to the initial insertion into the tissue. In this approach, an approximation of the initial deflection angle of the needle tip and the natural curvature of the needle path are utilized to simulate the insertion and predict the tip deflection prior to insertion. The model then calculates a modified needle entry point which accounts for the predicted tip deflection during the insertion. To create the model, we first collected a set of needle insertion data utilizing nonhomogeneous gelatin phantoms and ex vivo bovine tissue, using an electromagnetic (EM) tracking system and a needle with an EM sensor embedded at its tip. The collected data were then used to find the model parameters, namely, the initial deflection angle and the needle path curvature. After creating the model, a validation study consisting of two sets of insertions, one with and another without compensation, was carried out to evaluate the performance of our proposed model in compensating for the tip deflection error. The results demonstrate an average of 77% targeting accuracy improvement for insertions with modified entry points based on our model’s prediction compared with the uncompensated insertions.

A Model to Predict Deflection of an Active Tendon-Driven Notched Needle Inside Soft Tissue.

The last decade has witnessed major progress in the field of minimally invasive and robotic-assisted surgeries. Needle insertion, a minimally invasive technique, has proven its efficacy in procedures such as brachytherapy, ablation, drug delivery, and biopsy. Manual needle steering inside tissue is a challenging task due to complex needle-tissue interactions, needle and tissue movement, lack of actuation and control, as well as poor sensing and visualization. Recently, active tendon-driven notched needles, and robotic manipulation systems have been proposed to assist surgeons to guide the needles in desired trajectories toward target positions. This work introduces a new deflection model for the active tendon-driven notched needle steering inside soft tissue for intention to use in model-based robotic control. The model is developed to predict needle deflection in a single-layer tissue. To validate the proposed deflection model, five sets of needle insertion experiments with a bevel-tipped active needle into single-layer phantom tissues were performed. A real-time robot-assisted ultrasound tracking method was used to track the needle tip during needle insertion. It was shown that the model predicts needle deflection with an average error of 0.58 ± 0.14 mm for the bevel-tipped active needle insertion into a single-layer phantom tissue.

Steering Control Improvement of Active Surgical Needle Using Mosquito Proboscis-Inspired Cannula

Abstract Active needles have demonstrated superior tip deflection and improved accuracy compared to passive needles enhancing the efficacy of percutaneous needle insertion procedures. Successful navigation of these needles through tissues to reach targets relies on factors such as the actuation mechanism, tip shape, and surface geometry. In this study, we investigated the advantages of modifying the surface geometry of the active needle shaft, focusing on two improving crucial aspects: (a) needle tip deflection and (b) trajectory tracking during tissue insertion. Prior research had shown that modifying the surface geometry of passive needles reduced friction force, tissue displacement, and tissue damage. Building on this knowledge and being motivated by the surface geometry of mosquito proboscis, our study proposed a bio-inspired design modification on the active needle cannula. The active needle with the mosquito proboscis-inspired cannula was tested to measure the changes in insertion force, tip deflection, and trajectory tracking during polyvinyl chloride (PVC) phantom tissue insertions. Results showed that passive bevel-tip needles reduced insertion force by up to 10.67%. In active needles, tip deflection increased by 12.91% at 150 mm insertion depth when the cannula was modified. The bio-inspired cannula improved trajectory tracking error in the active needle by 39.00% while utilizing up to 17.65% lower control duty cycle. The enhancement of tip deflection and tracking control is expected to improve percutaneous procedures by achieving better patient outcomes and significantly mitigating the risk of complications.

A study on modeling the deflection of surgical needle during insertion into multilayer tissues

The use of subcutaneous and percutaneous needle and catheter insertions is standard in modern clinical practice. However, a common issue with bevel tip surgical needles is their tendency to deflect, causing them to miss the intended target inside the tissue. This study aims to understand the interaction between the needle and soft tissue and develop a model to predict the deflection of a bevel tip needle during insertion into multi-layered soft tissues. The study examined the mechanics of needle-tissue interaction and modeled the forces involved during insertion. The force model includes cutting force, deformation force, and friction between the needle and tissue. There was an 8%–23% difference between the total analytical and experimental force measurements. A modified Euler-Bernoulli beam elastic foundation theory was used to create an analytical model to predict the needle tip deflection in soft tissue. To validate the results, the analytical deflection model was then compared to the deflection from needle insertion experiments on multi-layered phantom tissues, showing a 9%–21% error between the two. While there is a slight discrepancy between the analytical and experimental results, the study shows that the proposed model can accurately predict needle tip deflection during insertion.

Towards a Procedure-Optimised Steerable Catheter for Deep-Seated Neurosurgery.

In recent years, steerable needles have attracted significant interest in relation to minimally invasive surgery (MIS). Specifically, the flexible, programmable bevel-tip needle (PBN) concept was successfully demonstrated in vivo in an evaluation of the feasibility of convection-enhanced delivery (CED) for chemotherapeutics within the ovine model with a 2.5 mm PBN prototype. However, further size reductions are necessary for other diagnostic and therapeutic procedures and drug delivery operations involving deep-seated tissue structures. Since PBNs have a complex cross-section geometry, standard production methods, such as extrusion, fail, as the outer diameter is reduced further. This paper presents our first attempt to demonstrate a new manufacturing method for PBNs that employs thermal drawing technology. Experimental characterisation tests were performed for the 2.5 mm PBN and the new 1.3 mm thermally drawn (TD) PBN prototype described here. The results show that thermal drawing presents a significant advantage in miniaturising complex needle structures. However, the steering behaviour was affected due to the choice of material in this first attempt, a limitation which will be addressed in future work.

Optical Fiber -Based Needle Shape Sensing: Three-channel Single Core vs. Multicore Approaches.

Bevel-tip needles are commonly utilized in percutaneous medical interventions where a curved insertion trajectory is required. To avoid deviation from the intended trajectory, needle shape sensing and tip localization is crucial in providing the operator with feedback. There is an abundance of previous work that investigate the medical application of fiber Bragg grating (FBG) sensors, but most works select only one specific type of fiber among the many available sensor options to integrate into their hardware designs. In this work, we compare two different types of FBG sensors under identical conditions and application, namely, acting as the sensor for needle insertion shape reconstruction. We built a three-channel single core needle and a seven-channel multicore fiber (MCF) needle and discuss the pros and cons of both constructions for shape sensing experiments into constant curvature jigs. The overall needle tip error is 1.23 mm for the single core needle and 2.08 mm for the multicore needle.

Lie-Group Theoretic Approach to Shape-Sensing Using FBG-Sensorized Needles Including Double-Layer Tissue and S-Shape Insertions.

Flexible bevel-tipped needles are often used for needle insertion in minimally-invasive surgical techniques due to their ability to be steered in cluttered environments. Shapesensing enables physicians to determine the location of needles intra-operatively without requiring radiation of the patient, enabling accurate needle placement. In this paper, we validate a theoretical method for flexible needle shape-sensing that allows for complex curvatures, extending upon a previous sensor-based model. This model combines curvature measurements from fiber Bragg grating (FBG) sensors and the mechanics of an inextensible elastic rod to determine and predict the 3D needle shape during insertion. We evaluate the model's shape sensing capabilities in C- and S-shape insertions in single-layer isotropic tissue, and C-shape insertions in two-layer isotropic tissue. Experiments on a four-active area, FBG-sensorized needle were performed in varying tissue stiffnesses and insertion scenarios under stereo vision to provide the 3D ground truth needle shape. The results validate a viable 3D needle shape-sensing model accounting for complex curvatures in flexible needles with mean needle shape sensing root-mean-square errors of 0.160 ± 0.055 mm over 650 needle insertions.

Rates of Epidural Blood Patch following Lumbar Puncture Comparing Atraumatic versus Bevel-Tip Needles Stratified for Body Mass Index.

Postdural puncture headache, a known complication of lumbar puncture, typically resolves with conservative management. Symptoms persist in a minority of patients, necessitating an epidural blood patch. One method of decreasing rates of postdural puncture headache is using atraumatic, pencil-point needles rather than bevel-tip needles. To the best of our knowledge, this is the first study comparing epidural blood patch rates between pencil- and bevel-tip needles with a subgroup analysis based on body mass index. This single-institution retrospective study identified 4435 patients with a recorded body mass index who underwent a lumbar puncture with a 22-ga pencil-tip Whitacre needle, a 20-ga bevel-tip Quincke needle, or a 22-ga Quincke needle. The groups were stratified by body mass index. We compared epidural blood patch rates between 22-ga pencil-tip Whitacre needles versus 22-ga Quincke needles and 22-ga Quincke needles versus 20-ga bevel-tip Quincke needles using the Fischer exact test and χ2 test. Postdural puncture headache necessitating an epidural blood patch was statistically more likely using a 22-ga Quincke needle in all patients (P < .001) and overweight (P = .03) and obese (P < .001) populations compared with using a 22-ga pencil-tip Whitacre needle. In the normal body mass index population, there was no statistically significant difference in epidural blood patch rates when using a 22-ga pencil-tip Whitacre needle compared with a 22-ga Quincke needle (P = .12). There was no significant difference in epidural blood patch rates when comparing a 22-ga Quincke needle versus a 20-ga bevel-tip Quincke needle in healthy (P = .70), overweight (P = .69), or obese populations (P = .44). Using a 22-ga pencil-tip Whitacre needle resulted in lower epidural blood patch rates compared with a 22-ga Quincke needle in all patients. Subgroup analysis demonstrated a statistically significant difference in epidural blood patch rates in overweight and obese populations, but not in patients with a normal body mass index.

Robotic Cytology using Extra-Fine Needles : -Proposal of Puncture Control Strategy for Increasing Collection Amount.

Fine needle aspiration cytology requires accurate needle insertion into a tumor and sufficient amount collection of samples, which highly depends on the skill of the physician. The advantage of the diagnosis is to minimize the tissue damage with the fine needle, while, when the amount of the sample sucked from the lesion is not enough for the definite diagnosis, the procedure has to be repeated until satisfying them. Although numerous research reported a robot-assisted insertion method to improve the accuracy of needle placement with fine needles, there was less research to address the efficient tissue collection. Ideally, the amount of the samples can be satisfied for the diagnosis even if an extra-fine needle (e.g. 25-gauge) is used. This paper proposes a novel needle insertion method for increasing the amount of the tissue sample with the extra-fine needle. The proposed insertion method comprises the round-trip insertion motion and trajectory rerouting with the nature of the bevel-tipped needle. The phantom study's result showed the equivalency of the aspiration amount between a physician's manual procedure with a 22-gauge needle and the proposed method with a 25-gauge needle (4.5 ± 1.0 mg vs 5.1 ± 0.7 mg). The results suggested that the proposed robotic aspiration method can increase the sampling amount with the extra-fine needle in the fine needle aspiration cytology.

Path Replanning for Orientation-Constrained Needle Steering.

Needle-based neurosurgical procedures require high accuracy in catheter positioning to achieve high clinical efficacy. Significant challenges for achieving accurate targeting are (i) tissue deformation (ii)clinical obstacles along the insertion path (iii) catheter control. We propose a novel path-replanner able to generate an obstacle-free and curvature bounded three-dimensional (3D) path at each time step during insertion, accounting for a constrained target pose and intraoperative anatomical deformation. Additionally, our solution is sufficiently fast to be used in a closed-loop system: needle tip tracking via electromagnetic sensors is used by the path-replanner to automatically guide the programmable bevel-tip needle (PBN) while surgical constraints on sensitive structures avoidance are met. The generated path is achieved by combining the "Bubble Bending" method for online path deformation and a 3D extension of a convex optimisation method for path smoothing. Simulation results performed on a realistic dataset show that our replanning method can guide a PBN with bounded curvature to a predefined target pose with an average targeting error of 0.65 ±0.46mm in position and 3.25 ±5.23 degrees in orientation under a deformable simulated environment. The proposed algorithm was also assessed in-vitro on a brain-like gelatin phantom, achieving a target error of 1.81 ±0.51mm in position and 5.9 ±1.42 degrees in orientation. The presented work assessed the performance of a new online steerable needle path-planner able to avoid anatomical obstacles while optimizing surgical criteria. This method is particularly suited for surgical procedures demanding high accuracy on the desired goal pose under tissue deformations and real-world inaccuracies.

Magnetic Control of a Flexible Needle in Neurosurgery.

Minimally invasive neurosurgery does not require large incisions and openings in the skull to access the desired brain region, which often results in a faster recovery with fewer complications than traditional open neurosurgery. For disorders treated by the implantation of neurostimulators and thermocoagulation probes, current procedures incorporate a straight rigid needle, which restricts surgical trajectories and limits the number of possible targets and degrees of freedom at the respective target. A steerable needle with a flexible body could overcome these limitations. In this paper, we present a flexible needle steering system with magnetic and fluoroscopic guidance for neurosurgical procedures. A permanent magnet at the proximal end of a flexible needle is steered by an external magnetic field, and the resultant tip-deflection angle bends the flexible body like a bevel-tip needle. We implemented a kinematic model for the magnetic needle derived from a nonholonomic bicycle model and a closed-loop control strategy with feed-forward and feed-back components using a chained-form transformation. The proposed needle steering method was investigated through in vitro and ex vivo experiments.

Biologically Inspired Surgical Needle Steering: Technology and Application of the Programmable Bevel-Tip Needle.

Percutaneous interventions via minimally invasive surgical systems can provide patients with better outcomes and faster recovery times than open surgeries. Accurate needle insertions are vital for successful procedures, and actively steered needles can increase system precision. Here, we describe how biology inspired the design of a novel Programmable Bevel-Tip Needle (PBN), mimicking the mechanics and control methods of certain insects ovipositors. Following an overview of our unique research and development journey, this paper explores our latest, biomimetic control of PBNs and its application to neurosurgery, which we validate within a simulated environment. Three modalities are presented, namely a Direct Push Controller, a Cyclic Actuation Controller, and a newly developed Hybrid Controller, which have been integrated into a surgical visual interface. The results of open loop, expert human-in-the-loop and a non-expert user study show that the Hybrid Controller is the best choice when considering system performance and the ability to lesson strain on the surrounding tissue which we hypothesis will result in less damage along the insertion tract. Over representative trajectories for neurosurgery using a Hybrid Controller, an expert user could reach a target along a 3D path with an accuracy of mm, and non-expert users mm, both clinically viable results and equivalent or better than the state-of-the-art actively steered needles over 3D paths. This paper showcases a successful example of a biologically inspired, actively steered needle, which has been integrated within a clinical interface and designed for seamless integration into the neurosurgical workflow.

Control of a flexible bevel-tipped needle using super-twisting controller based sliding mode observer

This article presents the implementation of super twisting controller based on higher-order sliding mode observer during needle maneuverability in minimal invasive surgery. To ensure chattering free and stable maneuverability of the needle in spite of external disturbances, a robust control strategy is necessary. Technically, it is cumbersome to develop the structure of super twisting controller with a third-order sliding mode observer leading to discontinuous control law. Henceforth, we propose the higher order (n+1) sliding mode observer with continuous control law. Our study involves the kinematic model of the bevel-tipped flexible needle to inculcate an accurate target reaching regulation of the needling system. Both simulation and experimental results with various bevel angles are presented to verify the proposed needle steering control methodology in biological tissue environment.

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

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°.

A Novel Path Planner for Steerable Bevel-Tip Needles to Reach Multiple Targets With Obstacles

Steerable bevel-tip needles offer higher maneuverability independent of insertion depth and consequently are preferred for many needle-steering applications compared with symmetric-tip needles. Using these needles, the clinician can reach previously inaccessible targets using traditional stiff needles, thus helping improve the efficiency of needle insertion procedures significantly. However, due to their nonholonomic kinematics inside biological tissue, path planning of these needles is complicated and requires a great deal of care. Rapidly exploring random-tree (RRT)-based approaches are proper candidates for intraoperative planning of needle motion due to their fast computation and simple implementations. They also work well in high-dimensional configuration spaces and under nonholonomic kinematic constraints, both of which are the characteristics of steerable bevel-tip needle motion inside soft tissue. We developed a new heuristic-based RRT planner to reach multiple targets inside soft tissue without having to completely retract, reorient, and reinsert the needle toward each separate target, resulting in significantly less tissue damage compared with the conventional sequential insertion of the needle toward each target. Moreover, the proposed planner can have real clinical applications, where the limited size of the workspace as well as the needle's limited natural curvature imposes significant limitations on the needle path-planning problem inside soft tissue. Simulations demonstrate the efficiency of the proposed planner. The maximum targeting error of all targets is 1 mm and the needle's inserted length is decreased up to 35% compared with the sequential insertion of the needle for each target.