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Robotic needle steering has become a topic of interest in intervention surgery. Yet, this surgical procedure poses challenges due to external disturbances and tissue movement. To address these challenges, several novel steering algorithms have been developed to guide the needle precisely from the entry point to the target point. However, some of these algorithms may cause additional trauma to patients. In this paper, we present a 3D optimal control algorithm for a curvature-controllable steerable (CCS) needle, aiming to achieve effective operations with minimal trauma. We derive a kinematics without duty cycle control strategy (needle shaft spin), propose a novel intraoperative motion planner for path replanning, and design a full-state feedback controller for accurate path tracking. A dynamic environment was simulated, and the optimal controller showed a better result (0.01 ± 0.01 mm) than the case (3.86 ± 1.32 mm) using a full-state feedback controller. The demonstration indicates that the optimal control system can safely, effectively, and accurately steer the needle to the target point in a dynamic environment. [ABSTRACT FROM AUTHOR]

In virtue of a curved insertion path inside tissues, needle steering techniques have revealed the potential with the assistance of medical robots and images. The superiority of this technique has been preliminarily verified with several maneuvers: target realignment, obstacle circumvention, and multi-target access. However, the momentum of needle steering approaches in the past decade leads to an open question—"How to choose an applicable needle steering approach for a specific clinical application?" This survey discusses this question in terms of design choices and clinical considerations, respectively. In view of design choices, this survey proposes a hierarchical taxonomy of current needle steering approaches. Needle steering approaches of different manipulations and designs are classified to systematically review the design choices and their influences on clinical treatments. In view of clinical consideration, this survey discusses the steerability and acceptability of the current needle steering approaches. On this basis, the pros and cons of the current needle steering approaches are weighed and their suitable applications are summarized. At last, this survey concluded with an outlook of the needle steering techniques, including the potential clinical applications and future developments in mechanical design. [ABSTRACT FROM AUTHOR]

A flexible needle has emerged as a crucial clinical technique in contemporary medical practices, particularly for minimally invasive interventions. Its applicability spans diverse surgical domains such as brachytherapy, cardiovascular surgery, neurosurgery and others. Notably, flexible needles find utility in biopsies requiring deep skin penetration to access infected areas. Despite its minimally invasive advantages, the precise guidance of the needle to its intended target, while avoiding damage to bones, blood vessels, organs and tissues, remains a significant challenge for researchers. Consequently, extensive research has been dedicated to enhancing the steering and accuracy of flexible needles. Here, we aim to elucidate the recent advancements, trends and perspectives in flexible needle steering models and path planning over the last 15 years. The discussed models encompass various types, including symmetric-tip needles, curved-tip needles, tendon-actuated needles, programmable needles and the innovative fracture-directed waterjet needles. Moreover, the paper offers a comprehensive analysis, comparing the trajectories followed by these needle models to attain the desired target with minimal tissue damage. By delving into these aspects, the paper contributes to a deeper understanding of the current landscape of flexible needle technology and guides future research directions in this dynamic field.

Robotic needle steering has become a topic of interest in intervention surgery. Yet, this surgical procedure poses challenges due to external disturbances and tissue movement. To address these challenges, several novel steering algorithms have been developed to guide the needle precisely from the entry point to the target point. However, some of these algorithms may cause additional trauma to patients. In this paper, we present a 3D optimal control algorithm for a curvature-controllable steerable (CCS) needle, aiming to achieve effective operations with minimal trauma. We derive a kinematics without duty cycle control strategy (needle shaft spin), propose a novel intraoperative motion planner for path replanning, and design a full-state feedback controller for accurate path tracking. A dynamic environment was simulated, and the optimal controller showed a better result (0.01 ± 0.01 mm) than the case (3.86 ± 1.32 mm) using a full-state feedback controller. The demonstration indicates that the optimal control system can safely, effectively, and accurately steer the needle to the target point in a dynamic environment.

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. [ABSTRACT FROM AUTHOR]

The surgical needle insertion process is widely applied in medical interference. During the insertion process, the inhomogeneity and denseness of the soft tissues make it tough to detect the essential tissue damage, a rupture occurs that contains huge forces and material deformations. This study is very important, as all the above-mentioned factors are very significant for modern invasive surgery so that the success rate of the surgery can increase and the patient recovers smoothly. This investigation intends to perform minimally invasive surgical (MIS) procedures and reduce the living tissue damage while performing the biopsy, PCNL, etc. A fracture mechanics method was analyzed to create a needle insertion model which can estimate the needle insertion force during inset in tissue-like PVA gel. The force model was calculated by needle insertion experimentally, and also estimated the needle tip geometry, and diameter influences the fracture toughness. Validate exp. results with simulation results and other papers. It is observed that needle diameter has a significant effect on fracture toughness, whereas the insertion velocity has a slight impact on the fracture toughness. During the rotational needle insertion process, the winds-up of the gel occurs and the diameter of the hole was increasing with increased rpm. Maximum insertion force was noticed in the 27 G needle at 5 mm/s. The interaction function will be less at the maximum fracture development region. Graphical abstract [ABSTRACT FROM AUTHOR]

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.

Abstract Background Brachytherapy involves placement of radioactive sources inside or near the tumour. For gynaecological cancer, recent developments, including 3D imaging and image-guided adaptive brachytherapy, have improved treatment quality and outcomes. However, for large or complex tumours, target coverage and local control with commercially available applicators remain suboptimal. Moreover, side effects are frequent and impact on quality of life. This signifies that brachytherapy treatment conformity can improve. Therefore, the aim of this study is to develop 3D printed personalised brachytherapy applicators with a custom vaginal topography and guided needle source channels, based on the patients’ anatomy. Methods Customised applicators were derived from MRI data of two gynaecological cancer patients. Needle channels were planned by the Radiation Oncologist during image segmentation. Applicators contained multi-curved channels for 6F needles (ProGuide, Elekta) and were manufactured using a digital light processing-based 3D printer. Needle channel radius constraints were measured by analysing needle insertion forces in a 3D printed template, and imposed on the designs. Results Two customised needle applicators are presented. Interstitial needle channels have tapered ends to increase needle protrusion angle accuracy. Additional structures were included to serve as anchor points in MR images for applicator and needle modelling and reconstruction during treatment planning. An insertion force analysis yielded a radius constraint of 35 mm to minimise the risk on needle jamming or buckling. For radii larger than 50 mm, no differences in insertion forces were found. Conclusion A novel method to design and produce vaginal topography-based 3D prints for personalised brachytherapy applicators, derived from patient MRI data, is presented. The applicators include curved needle channels that can be used for intracavitary and guided interstitial needle placement. Further spatial optimisation of brachytherapy source channels to the patient anatomy is expected to increase brachytherapy conformity and outcome.

This paper presents a new solution for 3D steering of flexible needles guided by 3D B-mode ultrasound imaging. It aims to realize a robust steering, by accounting for uncertainties, noise and tissue heterogeneities, while limiting tissue-related disturbances. The proposed solution features interconnected state observer, automatic needle tip segmentation and path planning algorithms. Measurement quality, state uncertainties and tissue heterogeneity are considered for robust needle steering with helical paths of variable curvature. Fast replanning allows for adaptability to unexpected disturbances. An experimental validation has been done through 62 insertions of 24 Gauge bevel-tip nitinol needles in various tissue. Results are promising, characterized by mean targeting errors of less than 1 mm in homogeneous phantoms, 1.5 ± 0.9 mm in heterogeneous phantoms and 1.7 ± 0.8 mm in ex-vivo tissue. This new approach is a step towards a precise and robust patient-specific gesture. [ABSTRACT FROM AUTHOR]

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. • Implementation of super twisting controller based on higher-order sliding mode observer during needle maneuverability. • The robust control algorithm is structured by the combination of super twisting algorithm with higher-order sliding mode observer to make the control input signal completely continuous. • The target reaching accuracy of a flexible needle reduces due to the out-of-plane motion because of its deflection that happens due to various factors such as dynamic needletissue interaction force, and tissue in-homogeneity. [ABSTRACT FROM AUTHOR]

In robot-assisted needle-insertion procedures, in order to steer a beveled-tip needle toward a target location, the needle tip pose is required to be used as feedback. Mathematically, the needle tip pose can be expressed as a position vector and a rotation matrix that represents the orientation of the needle. Using the traditional 2-D ultrasound imaging modality, since the images are low in resolution and the needles are small in diameter, only the position of the needle tip can be measured, and the orientation information cannot be directly extracted. This brief presents a nonlinear observer for estimating the needle tip orientation based on needle 3-D kinematic equations and the position data measured from ultrasound images. Assuming bounded inputs for the needle model, it is shown that the observation error remains bounded. The performance of the observer is shown in an observer/controller feedback loop. The stability and convergence of the observer/controller combination are shown by solving linear matrix inequality (LMI) optimizations. Experiments performed for different tissue types and different needle insertion goals verify the effectiveness of our method. [ABSTRACT FROM AUTHOR]

Penetration of a flexible and steerable needle into a soft target material is a complex problem to be modelled, involving several mechanical challenges. In the present paper, an adaptive finite element algorithm is developed to simulate the penetration of a steerable needle in brain-like gelatine material, where the penetration path is not predetermined. The geometry of the needle tip induces asymmetric tractions along the tool–substrate frictional interfaces, generating a bending action on the needle in addition to combined normal and shear loading in the region where fracture takes place during penetration. The fracture process is described by a cohesive zone model, and the direction of crack propagation is determined by the distribution of strain energy density in the tissue surrounding the tip. Simulation results of deep needle penetration for a programmable bevel-tip needle design, where steering can be controlled by changing the offset between interlocked needle segments, are mainly discussed in terms of penetration force versus displacement along with a detailed description of the needle tip trajectories. It is shown that such results are strongly dependent on the relative stiffness of needle and tissue and on the tip offset. The simulated relationship between programmable bevel offset and needle curvature is found to be approximately linear, confirming empirical results derived experimentally in a previous work. The proposed model enables a detailed analysis of the tool–tissue interactions during needle penetration, providing a reliable means to optimise the design of surgical catheters and aid pre-operative planning. [ABSTRACT FROM AUTHOR]

Purpose: Flexible needle insertion is an important minimally invasive surgery approach for biopsy and radio-frequency ablation. This approach can minimize intraoperative trauma and improve postoperative recovery. We propose a new path planning framework using multi-goal deep reinforcement learning to overcome the difficulties in uncertain needle–tissue interactions and enhance the robustness of robot-assisted insertion process. Methods: This framework utilizes a new algorithm called universal distributional Q-learning (UDQL) to learn a stable steering policy and perform risk management by visualizing the learned Q-value distribution. To further improve the robustness, universal value function approximation is leveraged in the training process of UDQL to maximize generalization and connect to diagnosis by adapting fast re-planning and transfer learning. Results: Computer simulation and phantom experimental results show our proposed framework can securely steer flexible needles with high insertion accuracy and robustness. The framework also improves robustness by providing distribution information to clinicians for diagnosis and decision making during surgery. Conclusions: Compared with previous methods, the proposed framework can perform multi-target needle insertion through single insertion point qunder continuous state space model with higher accuracy and robustness. [ABSTRACT FROM AUTHOR]

In this paper, we design a set of two-dimensional (2-D) needle steering controllers used to minimize the 3-D deflection of a flexible, bevel-tipped needle. The controllers are based on a nonlinear design tool known as integrator-backstepping. The needle's deflection is split into its two 2-D planar problems, each of which is then governed by its own, separate controller. One controller, called vertical deflection control (VDC), steers the needle so that it deflects primarily along the vertical plane. The second controller, called horizontal deflection control (HDC), steers the needle so that it deflects primarily along the horizontal plane. Our 3-D steering algorithm combines the effect of these two controllers based on the current magnitude of the deflection along each plane. Using an 18 gauge brachytherapy needle, we tested our proposed method on a phantom tissue composed of liquid plastic, and a two-layer biological tissue formed of gelatin and ex vivo beef. Without needle steering, the average needle deflection was 11.2 mm. Using the proposed 3-D needle steering technique, the deflection decreased to an average of 0.5 mm. [ABSTRACT FROM AUTHOR]

Objective: Needle insertion is a common component of most diagnostic and therapeutic interventions. Needles with asymmetrically sharpened points such as the bevel point are ubiquitous. Their insertion path is typically curved due to the rudder effect at the point. However, the common planned path is straight, leading to targeting errors. We present a simple technique that may substantially reduce these errors. The method was inspired by practical experience, conceived mathematically, and refined experimentally. Methods: Targeting errors are reduced by flipping the bevel on the opposite side (rotating the needle 180° about its axis), at a certain depth during insertion. The ratio of the flip depth to the full depth of insertion is defined as the flip depth ratio (FDR). Based on a model, FDR is constant 0.3. Results: Experimentally, the ratio depends on the needle diameter, 0.35 for 20Ga and 0.45 for 18Ga needles. Thinner needles should be flipped a little shallower, but never less than 0.3. Conclusion: Practically, a physician may expect to reduce ∼80% of needle deflection errors by simply flipping the needle. The technique may be used by hand or with guidance devices. [ABSTRACT FROM AUTHOR]

This paper introduces a new approach for the control of a robotic system interacting with deformable structures. The method is applied to needle insertion procedures, which are among the least invasive surgical approaches to access deep internal structures with sometimes poor access conditions. Yet, during the insertion both tissues and needles deform resulting in a displacement of targets identified at the planning step and significantly raising the technical difficulty of these approaches. Robotic assistance may offer new possibilities to enforce the accuracy of the needle's positioning, but the deformation of tissues remains an open problem. In this paper, we propose a numerical approach where finite-element (FE) models are used in a close-control robotic loop. We introduce a complete forward simulation of deformable structures (needle and environment) and constraint-based interaction models allowing for the simulation of needle insertion and complex nonlinear phenomena (friction, puncture, and insertion) at a high frequency. For the control, we numerically derive the so-called Jacobian of the Simulation using an inverse method. The most original aspect of this paper lies in the fact that inverse steps are performed in constraints space, allowing this way for fast estimation of the Jacobian (i.e., between 40 and 100 Hz). The method is validated both numerically and experimentally using a flexible needle inserted inside a deformable foam. We show that the robot is able to follow a given trajectory, defined during the planning step, taking into account any occurring deformation of both the needle and the foam during the insertion; without any need for tracking the needle neither the target nor the trajectory. [ABSTRACT FROM AUTHOR]

Purpose: In the context of minimally invasive neurosurgery, steerable needles such as the one developed within the Horizon2020-funded EDEN2020 project (Frasson et al. in Proc Inst Mech Eng Part H J Eng Med 224(6):775-88, 2010. 10.1243/09544119JEIM663; Secoli and y Baena in IEEE international conference on robotics and automation, 2013) aspire to address the clinical challenge of better treatment for cancer patients. The direct, precise infusion of drugs in the proximity of a tumor has been shown to enhance its effectiveness and diffusion in the surrounding tissue (Vogelbaum and Aghi in Neuro-Oncology 17(suppl 2):ii3-ii8, 2015. 10.1093/neuonc/nou354). However, planning for an appropriate insertion trajectory for needles such as the one proposed by EDEN2020 is challenging due to factors like kinematic constraints, the presence of complex anatomical structures such as brain vessels, and constraints on the required start and target poses.Methods: We propose a new parallelizable three-dimensional (3D) path planning approach called Adaptive Hermite Fractal Tree (AHFT), which is able to generate 3D obstacle-free trajectories that satisfy curvature constraints given a specified start and target pose. The AHFT combines the Adaptive Fractal Tree algorithm's efficiency (Liu et al. in IEEE Robot Autom Lett 1(2):601-608, 2016. 10.1109/LRA.2016.2528292) with optimized geometric Hermite (Yong and Cheng in Comput Aided Geom Des 21(3):281-301, 2004. 10.1016/j.cagd.2003.08.003) curves, which are able to handle heading constraints.Results: Simulated results demonstrate the robustness of the AHFT to perturbations of the target position and target heading. Additionally, a simulated preoperative environment, where the surgeon is able to select a desired entry pose on the patient's skull, confirms the ability of the method to generate multiple feasible trajectories for a patient-specific case.Conclusions: The AHFT method can be adopted in any field of application where a 3D path planner with kinematic and heading constraints on both start and end poses is required. [ABSTRACT FROM AUTHOR]

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 0.70±0.69 mm, and non-expert users 0.97±0.72 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.

A flexible needle has emerged as a crucial clinical technique in contemporary medical practices, particularly for minimally invasive interventions. Its applicability spans diverse surgical domains such as brachytherapy, cardiovascular surgery, neurosurgery and others. Notably, flexible needles find utility in biopsies requiring deep skin penetration to access infected areas. Despite its minimally invasive advantages, the precise guidance of the needle to its intended target, while avoiding damage to bones, blood vessels, organs and tissues, remains a significant challenge for researchers. Consequently, extensive research has been dedicated to enhancing the steering and accuracy of flexible needles. Here, we aim to elucidate the recent advancements, trends and perspectives in flexible needle steering models and path planning over the last 15 years. The discussed models encompass various types, including symmetric-tip needles, curved-tip needles, tendon-actuated needles, programmable needles and the innovative fracture-directed waterjet needles. Moreover, the paper offers a comprehensive analysis, comparing the trajectories followed by these needle models to attain the desired target with minimal tissue damage. By delving into these aspects, the paper contributes to a deeper understanding of the current landscape of flexible needle technology and guides future research directions in this dynamic field.

Abstract This paper presents a new method for estimating the needle pose and curvature in the context of robotically steered needles. The needle tip trajectory is represented by a modified unicycle kinematic model. A multi-rate unscented Kalman filter is proposed for the first time in needle steering to fuse asynchronous data coming from 3D B-mode ultrasound images, robot sensors and pre-operative elastography measurements. To demonstrate it, 51 unconstrained 3D insertions have been made in various media. The instantaneous localisation error is smaller than 0.6 mm and the prediction of the final tip position is smaller than 2 mm based on the observation of the first 2 cm of 8 cm deep insertions. Highlights • This paper presents an observer for 3D needle steering and 3D ultrasound imaging. • This observer enables multi-sensor, asynchronous measurements. • Changes in medium elasticity are taken into account for online curvature estimation. • The needle tip Cartesian position is precisely estimated in noisy ultrasound images. • The tip final position is precisely estimated after observing 2 cm of 8 cm insertions. [ABSTRACT FROM AUTHOR]

Robotic-assisted needle steering can enhance the accuracy of needle-based interventions. Application of current needle steering techniques are restricted by the limited deflection curvature of needles. Here, a novel steerable needle with improved curvature is developed and used with an online motion planner to steer the needle along curved paths inside tissue. The needle is developed by carving series of small notches on the shaft of a standard needle. The notches decrease the needle flexural stiffness, allowing the needle to follow tightly curved paths with small radius of curvature. In this paper, first, a finite element model of the notched needle deflection in tissue is presented. Next, the model is used to estimate the optimal location for the notches on needle's shaft for achieving a desired curvature. Finally, an ultrasound-guided motion planner for needle steering inside tissue is developed and used to demonstrate the capability of the notched needle in achieving high curvature and maneuvering around obstacles in tissue. We simulated a clinical scenario in brachytherapy, where the target is obstructed by the pubic bone and cannot be reached using regular needles. Experimental results show that the target can be reached using the notched needle with a mean accuracy of 1.2 mm. Thus, the proposed needle enables future research on needle steering toward deeper or more difficult-to-reach targets. [ABSTRACT FROM AUTHOR]

Intra-operative imaging is sometimes available to assist needle biopsy, but typical open-loop insertion does not account for unmodeled needle deflection or target shift. Closed-loop image-guided compensation for deviation from an initial straight-line trajectory through rotational control of an asymmetric tip can reduce targeting error. Incorporating robotic closed-loop control often reduces physician interaction with the patient, but by pairing closed-loop trajectory compensation with hands-on cooperatively controlled insertion, a physician’s control of the procedure can be maintained while incorporating benefits of robotic accuracy. A series of needle insertions were performed with a typical 18G needle using closed-loop active compensation under both fully autonomous and user-directed cooperative control. We demonstrated equivalent improvement in accuracy while maintaining physician-in-the-loop control with no statistically significant difference (p > 0.05) in the targeting accuracy between any pair of autonomous or individual cooperative sets, with average targeting accuracy of 3.56 mmrms. With cooperatively controlled insertions and target shift between 1 and 10 mm introduced upon needle contact, the system was able to effectively compensate up to the point where error approached a maximum curvature governed by bending mechanics. These results show closed-loop active compensation can enhance targeting accuracy, and that the improvement can be maintained under user directed cooperative insertion. [ABSTRACT FROM AUTHOR]

Robotic control of needle bending aims at increasing the precision of percutaneous procedures. Ultrasound feedback is preferable for its clinical ease of use, cost and compactness but raises needle detection issues. In this paper, we propose a complete system dedicated to robotized guidance of a flexible needle under 3D ultrasound imaging. This system includes a medical robot dedicated to transperineal needle positioning and insertion, a rapid path planning for needle steering using bevel-tip needle natural curvature in tissue, and an ultrasound-based automatic needle detection algorithm. Since ultrasound-based automatic needle steering is often made difficult by the needle localization in biological tissue, we quantify the benefit of using flexible echogenic needles for robotized guidance under 3D ultrasound. The “echogenic” term refers to the etching of microstructures on the needle shaft. We prove that these structures improve needle visibility and detection robustness in ultrasound images. We finally present promising results when reaching targets using needle steering. The experiments were conducted with various needles in different media (synthetic phantoms and ex vivo biological tissue). For instance, with nitinol needles the mean accuracy is 1.2 mm (respectively 3.8 mm) in phantoms (resp. biological tissue). [ABSTRACT FROM AUTHOR]

Purpose: Flexible needle has the potential to accurately navigate to a treatment region in the least invasive manner. We propose a new planning method using Markov decision processes (MDPs) for flexible needle navigation that can perform robust path planning and steering under the circumstance of complex tissue-needle interactions.Methods: This method enhances the robustness of flexible needle steering from three different perspectives. First, the method considers the problem caused by soft tissue deformation. The method then resolves the common needle penetration failure caused by patterns of targets, while the last solution addresses the uncertainty issues in flexible needle motion due to complex and unpredictable tissue-needle interaction.Results: Computer simulation and phantom experimental results show that the proposed method can perform robust planning and generate a secure control policy for flexible needle steering. Compared with a traditional method using MDPs, the proposed method achieves higher accuracy and probability of success in avoiding obstacles under complicated and uncertain tissue-needle interactions. Future work will involve experiment with biological tissue in vivo.Conclusion: The proposed robust path planning method can securely steer flexible needle within soft phantom tissues and achieve high adaptability in computer simulation. [ABSTRACT FROM AUTHOR]

Image-guided robot-assisted minimally invasive surgery is an important medicine procedure used for biopsy or local target therapy. In order to reach the target region not accessible using traditional techniques, long and thin flexible needles are inserted into the soft tissue which has large deformation and nonlinear characteristics. However, the detection results and therapeutic effect are directly influenced by the targeting accuracy of needle steering. For this reason, the needle-tissue interactive mechanism, path planning, and steering control are investigated in this review by searching literatures in the last 10 years, which results in a comprehensive overview of the existing techniques with the main accomplishments, limitations, and recommendations. Through comprehensive analyses, surgical simulation for insertion into multi-layer inhomogeneous tissue is verified as a primary and propositional aspect to be explored, which accurately predicts the nonlinear needle deflection and tissue deformation. Investigation of the path planning of flexible needles is recommended to an anatomical or a deformable environment which has characteristics of the tissue deformation. Nonholonomic modeling combined with duty-cycled spinning for needle steering, which tracks the tip position in real time and compensates for the deviation error, is recommended as a future research focus in the steering control in anatomical and deformable environments. Graphical abstract a Insertion force when the needle is inserted into soft tissue. b Needle deflection model when the needle is inserted into soft tissue [68]. c Path planning in anatomical environments [92]. d Duty-cycled spinning incorporated in nonholonomic needle steering [64]. [ABSTRACT FROM AUTHOR]

Global modelling has traditionally been the approach taken to estimate needle deflection in soft tissue. In this paper, we propose a new method based on local data-driven modelling of needle deflection. External measurement of needle-tissue interactions is collected from several insertions in ex vivo tissue to form a cloud of data. Inputs to the system are the needle insertion depth, axial rotations, and the forces and torques measured at the needle base by a force sensor. When a new insertion is performed, the just-in-time learning method estimates the model outputs given the current inputs to the needle-tissue system and the historical database. The query is compared to every observation in the database and is given weights according to some similarity criteria. Only a subset of historical data that is most relevant to the query is selected and a local linear model is fit to the selected points to estimate the query output. The model outputs the 3D deflection of the needle tip and the needle insertion force. The proposed approach is validated in ex vivo multilayered biological tissue in different needle insertion scenarios. Experimental results in five different case studies indicate an accuracy in predicting needle deflection of 0.81 and 1.24 mm in the horizontal and vertical lanes, respectively, and an accuracy of 0.5 N in predicting the needle insertion force over 216 needle insertions. [ABSTRACT FROM AUTHOR]

This paper presents a sliding-based method for steering beveled-tip needles in a 3D environment. The controller determines the needle roll angle using the needle tip position deviation, obtained from ultrasound images, and its first time derivative. The stability of the closed-loop system is analyzed using 3D unicycle model for the needle, as a result of which parameter constraints are derived. In this method the needle is steered by performing adjustments of needle orientation, which reduces the tissue trauma and injury. The performance of this method is verified by performing experiments using phantom tissue for environments with and without obstacles. [ABSTRACT FROM AUTHOR]

The performance of needle-based interventions depends on the accuracy of needle tip positioning. Here, a novel needle steering strategy is proposed that enhances accuracy of needle steering. In our approach the surgeon is in charge of needle insertion to ensure the safety of operation, while the needle tip bevel location is robotically controlled to minimize the targeting error. The system has two main components: (1) a real-time predictor for estimating future needle deflection as it is steered inside soft tissue, and (2) an online motion planner that calculates control decisions and steers the needle toward the target by iterative optimization of the needle deflection predictions. The predictor uses the ultrasound-based curvature information to estimate the needle deflection. Given the specification of anatomical obstacles and a target from preoperative images, the motion planner uses the deflection predictions to estimate control actions, i.e., the depth(s) at which the needle should be rotated to reach the target. Ex-vivo needle insertions are performed with and without obstacle to validate our approach. The results demonstrate the needle steering strategy guides the needle to the targets with a maximum error of 1.22 mm. [ABSTRACT FROM AUTHOR]

Steerable needles can potentially improve the effectiveness of diagnostic and therapeutic procedures, such as biopsy and cancer treatment, by increasing the targeting accuracy and reaching previously inaccessible targets. A discrete potential field algorithm based on three dimensional (3D) anatomical structures is proposed in this paper to plan the needle path in minimally invasive surgery. A 3D kinematic model of needle steering is formulated using Lie group theory. Model parameters are fitted using experimental data acquired via a 2-degree of freedom robotic device and an ultrasound imaging device. To execute the paths with variable curvatures, the model is incorporated with duty cycled spinning. Empirical formula between needle curvature and duty cycled factor is obtained through insertion experiments. To improve the targeting accuracy, a path tracking algorithm is developed by correcting for the heading error and cross-track error of the needle tip. The targeting error of the simulation is 0.29 mm. We experimentally evaluate the path tracking model and it achieves an average targeting error of 1.15 ± 0.56 mm in 3D environments with anatomical obstacles. The results of simulation are in agreement with steering experiments, showing that the discrete potential field algorithm and path tracking model have the potential to improve targeting accuracy and advance the therapeutic and diagnostic procedures. [ABSTRACT FROM AUTHOR]

This study aims to develop and evaluate a manually controlled steerable needle that is compatible with and visible on MRI to facilitate full intra-procedural control and accurate navigation in percutaneous interventions. The steerable needle has a working channel that provides a lumen to a cutting stylet or a therapeutic instrument. A steering mechanism based on cable-operated compliant elements is integrated in the working channel. The needle can be steered by adjusting the orientation of the needle tip through manipulation of the handle. The steering mechanism is evaluated by recording needle deflection at constant steering angles. A steering angle of 20.3° results in a deflection of 9.1-13.3 mm in gelatin and 4.6-18.9 mm in porcine liver tissue at an insertion depth of 60 mm. Additionally, the possibility to control the needle path under MRI guidance is evaluated in a gelatin phantom. The needle can be steered to targets at different locations while starting from the same initial position and orientation under MRI guidance with generally available sequences. The steerable needle offers flexibility to the physician in control and choice of the needle path when navigating the needle toward the target position, which allows for optimization of individual treatment and may increase target accuracy. [ABSTRACT FROM AUTHOR]

Robotics-assisted needle steering can enhance targeting accuracy in percutaneous interventions. This paper presents a novel dynamical model for robotically controlled needle steering. This is the first model that predicts both needle shape and tip position in soft tissue, and accepts needle insertion velocity, needle 180^\circ axial rotation, and needle base force/torque as inputs. A hybrid formulation of needle steering dynamics in soft tissue is presented, which considers the needle as a two-body rigid/flexible coupled system composed of a moving, discrete, and rigid part attached to a vibrating compliant part that is subject to external excitation forces. The former is the carrier representing the surgeon's hand or the needle inserting robot, while the latter is a beam modeling the continuous deflection of the needle inside tissue. A novel time-delayed tissue model and a fracture mechanics-based model are developed to model the tissue reaction forces and cutting force at the needle tip, respectively. Experiments are performed on synthetic and ex vivo animal tissues to identify the model parameters and validate the needle steering model. The maximum error of the 2-D model in predicting the needle tip position in the insertion plane was 1.59 mm in the case of no axial rotation and 0.74 mm with axial rotation. [ABSTRACT FROM PUBLISHER]