Robotic Needle Insertion Research Articles

Robotic Needle Insertion With 2D Ultrasound–3D CT Fusion Guidance

Robotic Needle Insertion With 2D Ultrasound–3D CT Fusion Guidance

Robotic MR-guided high dose rate brachytherapy needle implantation in the prostate (ROBiNSon)—a proof-of-concept study

Objective. A robotic needle implant device for MR-guided high-dose-rate (HDR) prostate brachytherapy was developed. This study aimed to assess the feasibility and spatial accuracy of HDR brachytherapy using the robotic device, for a single intraprostatic target point. Approach. Five patients were treated from November 2019–June 2022 with the robot. The robot fits a 1.5 T MR scanner and the needle can be shifted and angulated. An intraprocedural MR scan was fused with the diagnostic MR and one preplanned needle position was selected for robotic insertion. The needle entry point and angles were set for a needle tip target point within the intraprostatic target volume. The needle was tapped stepwise towards the target point pneumatically. Final needle position was verified with MR, followed by plan optimization and dose delivery. Any remaining planned needles were inserted manually. Needle tip to geometrical target error (NTG-error) was defined as the deviation of the actual tip position relative to the predefined geometric target point, using MR-coordinates. Needle tip to treatment target error (NTT-error) was defined as the deviation of the actual tip position relative to the treatment target point, using fused MR-images pre- and post-needle implantation taking into account prostate deformation. Difference between NTT-error and NTG-error and fiducial marker shifts indicated prostate movement. For determining prostate deformation, the Jaccard index and prostate volumes were assessed. Main results. The robotic device was able to tap the needle to the planned depth for all patients. Mean robotic procedure duration was 142 min. NTG-error was 3.2 (range 1.1–6.7) mm and NTT-error 4.5 (range 2.6–9.6) mm. Marker displacements were smaller than 3 mm. No treatment-related acute toxicity was reported. Feasibility of needle placement within the prostate was considered adequate. Significance. MR-guided robotic needle insertion is feasible with a mean geometric accuracy of 3.2 mm and <3 mm prostate movement.

Design and Evaluation of an Eye Mountable AutoDALK Robot for Deep Anterior Lamellar Keratoplasty.

Partial-thickness corneal transplants using a deep anterior lamellar keratoplasty (DALK) approach has demonstrated better patient outcomes than a full-thickness cornea transplant. However, despite better clinical outcomes from the DALK procedure, adoption of the technique has been limited because the accurate insertion of the needle into the deep stroma remains technically challenging. In this work, we present a novel hands-free eye mountable robot for automatic needle placement in the cornea, AutoDALK, that has the potential to simplify this critical step in the DALK procedure. The system integrates dual light-weight linear piezo motors, an OCT A-scan distance sensor, and a vacuum trephine-inspired design to enable the safe, consistent, and controllable insertion of a needle into the cornea for the pneumodissection of the anterior cornea from the deep posterior cornea and Descemet's membrane. AutoDALK was designed with feedback from expert corneal surgeons and performance was evaluated by finite element analysis simulation, benchtop testing, and ex vivo experiments to demonstrate the feasibility of the system for clinical applications. The mean open-loop positional deviation was 9.39 µm, while the system repeatability and accuracy were 39.48 µm and 43.18 µm, respectively. The maximum combined thrust of the system was found to be 1.72 N, which exceeds the clinical penetration force of the cornea. In a head-to-head ex vivo comparison against an expert surgeon using a freehand approach, AutoDALK achieved more consistent needle depth, which resulted in fewer perforations of Descemet's membrane and significantly deeper pneumodissection of the stromal tissue. The results of this study indicate that robotic needle insertion has the potential to simplify the most challenging task of the DALK procedure, enable more consistent surgical outcomes for patients, and standardize partial-thickness corneal transplants as the gold standard of care if demonstrated to be more safe and more effective than penetrating keratoplasty.

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.

OC-0295 MRI-guided robotic needle insertion for prostate brachytherapy: proof of principle patient study

OC-0295 MRI-guided robotic needle insertion for prostate brachytherapy: proof of principle patient study

Decision Method of Optimal Needle Insertion Angle for Dorsal Hand Intravenous Robot.

In the context of COVID-19, the research on various aspects of the venipuncture robot field has become increasingly hot, but there has been little research on robotic needle insertion angles, primarily performed at a rough angle. This will increase the rate of puncture failure. Furthermore, there is sometimes significant pain due to the patients' differences. This paper investigates the optimal needle entry angle decision for a dorsal hand intravenous injection robot. The dorsal plane of the hand was obtained by a linear structured light scan, which was used as a basis for calculating the needle entry angle. Simulation experiments were also designed to determine the optimal needle entry angle. Firstly, the linear structured optical system was calibrated and optimized, and the error function was constructed and solved iteratively by the optimization method to eliminate measurement error. Besides, the dorsal hand was scanned to obtain the spatial point clouds of the needle entry area, and the least squares method was used to fit it to obtain the dorsal hand plane. Then, the needle entry angle was calculated based on the needle entry area plane. Finally, the changes in the penetration force under different needle entry angles were analyzed to determine the optimal needle insertion angle. According to the experimental results, the average error of the optimized structured light plane position was about 0.1 mm, which meets the needs of the project, and a large angle should be properly selected for needle insertion during the intravenous injection.

Robot-Assisted Needle Insertion for CT-Guided Puncture: Experimental Study with a Phantom and Animals.

This study aimed to evaluate the accuracy and safety of robotic CT-guided needle insertion in phantom and animal experiments. A robotic system was developed for CT-guided needle insertion. For the phantom experiment, a specially made phantom containing multiple spherical was used. 15 robotic and manual insertions were conducted, and the accuracy, time, number of needle insertions, and radiation dose were compared between the robotic and manual insertion using Student's t-test. For the animal experiment, 20 robotic needle insertions were attempted toward simulated pulmonary nodules in the swine lung. The accuracy and safety of robotic CT-guided needle insertions were evaluated. In the phantom experiment, the mean accuracies of manual and robotic insertion were 1.8 ± 0.3mm and 1.9 ± 0.2mm. The accuracy of robotic needle insertion had no significant difference with manual needle insertion, but the number of needle insertions and radiation dose of the robotic needle placement significantly decreased compared to manual needle placement. In the animal experiment, the mean accuracy of the robotic needle insertion was 3.8 ± 1.3mm. The time for the whole needle insertion was 14.4 ± 4.8min. The whole robotic needle insertions were safe and only one mild pneumothorax occurred. CT-guided robotic needle insertion showed accuracy comparable to manual needle insertion, but the number of needle insertions, confirmatory scans, and radiation exposure had been reduced significantly. In future, we will further apply the robotic system to clinical experiments.

Kinematics modelling and dynamics analysis of an SMA-actuated active flexible needle for feedback-controlled manipulation in phantom.

Percutaneous needle-based procedures such as prostate brachytherapy demands for accurate placement of the needle tip at target locations. Recently, robotic needle insertion systems have been made available to help physicians in needle guidance and control inside tissue. It is often challenging to obtain an accurate and real-time position of the needle tip in clinical practice using medical imaging techniques. However, this information is vital for closed-loop control of the needles inside tissue. This work presents an SMA-actuated active flexible needle that is controlled inside a phantom without a need for a position sensor or a medical imaging device. The needle tip position feedback is found using shape sensing capabilities of the embedded SMA-wire actuators and a force sensor at the needle base. Three models were characterized and used to estimate needle tip position in real time. The control scheme was then tested on the active flexible needle to track a desired triangular trajectory in a phantom. It was shown that the control scheme presented in this work was able to manipulate the needle in this path with a reasonable accuracy.

An admittance-controlled amplified force tracking scheme for collaborative lumbar puncture surgical robot system.

The accurate sensing and display of the delicate needle-tissue interaction force to the operator is desirable for needle insertion procedures. It not only plays a significant role in the surgical treatment effect, but also has a great significance in improving surgical safety and reducing the incidence of complications. However, the direct detection of the interaction force between the tissue and needle tip by placement of a force sensor is challenging owing to the constraints of miniaturisation, cost, and sterilisation. In this study, a new position-based force-to-motion controller with magnified force feedback is presented to provide augmented force perception to the operator during needle insertion on the soft tissue. Furthermore, the demonstration to the position-based low level motion controller is more suitable for needle insertion surgical requirements in the cooperative robotic system. The proposed controller was experimentally validated by a collaborative lumbar puncture robotics system. Additionally, to provide hand tremor rejection for the stable manipulation of the puncture needle, it was demonstrated that the proposed amplified feedback force controller allowed a safer object interaction with the robotic needle insertion assistance. The results of the experiment show that a desirable interaction force profile is perceived by the operator during the overall insertion task operation. The admittance gain for the simplified admittance controller has a significant impact on the operator's ability to accurately control the applied force.

Penetration detection with intention recognition for cooperatively controlled robotic needle insertion

In lumbar puncture surgeries, force and position information throughout the insertion procedure is vital for needle tip localization, because it reflects different tissue properties. Especially in pediatric cases, the changes are always insignificant for surgeons to sense the crucial feeling of loss of resistance. In this study, a robot system is developed to tackle the major clinical difficulties. Four different control algorithms with intention recognition ability are applied on a novel lumbar puncture robot system for better human–robot cooperation. Specific penetration detection based on force and position derivatives captures the feeling of loss of resistance, which is deemed crucial for needle tip location. Kinematic and actuation modeling provides a clear description of the hardware setup. The control algorithm experiment compares the human–robot cooperation performance of proposed algorithms. The experiment also dictates the clear role of designed penetration detection criteria in capturing the penetration, improving the success rate, and ensuring operational safety.

Collaborative robot assisted smart needle placement

Abstract Needles are key tools to realize minimally invasive interventions. Physicians commonly rely on subjectively perceived insertion forces at the distal end of the needle when advancing the needle tip to the desired target. However, detecting tissue transitions at the distal end of the needle is difficult since the sensed forces are dominated by shaft forces. Disentangling insertion forces has the potential to substantially improve needle placement accuracy.We propose a collaborative system for robotic needle insertion, relaying haptic information sensed directly at the needle tip to the physician by haptic feedback through a light weight robot. We integrate optical fibers into medical needles and use optical coherence tomography to image a moving surface at the tip of the needle. Using a convolutional neural network, we estimate forces acting on the needle tip from the optical coherence tomography data. We feed back forces estimated at the needle tip for real time haptic feedback and robot control. When inserting the needle at constant velocity, the force change estimated at the tip when penetrating tissue layers is up to 94% between deep tissue layers compared to the force change at the needle handle of 2.36 %. Collaborative needle insertion results in more sensible force change at tissue transitions with haptic feedback from the tip (49.79 ± 25.51)% compared to the conventional shaft feedback (15.17 ± 15.92) %. Tissue transitions are more prominent when utilizing forces estimated at the needle tip compared to the forces at the needle shaft, indicating that a more informed advancement of the needle is possible with our system.

OC-0105 MRI-guided robotic needle insertion for prostate brachytherapy: proof of principle study in patients

OC-0105 MRI-guided robotic needle insertion for prostate brachytherapy: proof of principle study in patients

Respiratory Compensated Robot for Liver Cancer Treatment: Design, Fabrication, and Benchtop Characterization

Hepatocellularcarcinoma (HCC) is one of the leading causes of cancer-related deaths in the world. Radiofrequency ablation (RFA) is an effective method for treating tumors less than 5 cm. However, manually placing the RFA needle at the site of the tumor is challenging due to the complicated respiratory induced motion of the liver. This article presents the design, fabrication, and benchtop characterization of a patient mounted, respiratory compensated robotic needle insertion platform to perform percutaneous needle interventions. The robotic platform consists of a four degrees of freedom (DoF) dual-stage Cartesian platform used to control the pose of a one-DoF needle insertion module. The active needle insertion module consists of a 3-D printed flexible fluidic actuator capable of providing a steplike, grasp&#x2013;insert&#x2013;release actuation that mimics the manual insertion procedure. Force characterization of the needle insertion module indicates that the device is capable of producing 22.6 &#x00B1; 0.40 N before the needle slips between the grippers. Static phantom targeting experiments indicate a positional error of 1.14 &#x00B1; 0.30 mm and orientational error of 0.99&#x00B0; &#x00B1; 0.36&#x00B0;. Static <i>ex vivo</i> porcine liver targeting experiments indicate a positional error of 1.22 &#x00B1; 0.31 mm and orientational error of 1.16&#x00B0; &#x00B1; 0.44&#x00B0;. Dynamic targeting experiments with the proposed active motion compensation in dynamic phantom and <i>ex vivo</i> porcine liver show 66.3&#x0025; and 69.6&#x0025; positional accuracy improvement, respectively. Future work will continue to develop this platform with the long-term goal of applying the system to RFA for HCC.

Design and control of a bionic needle puncture robot.

The application of minimally invasive interventional breast surgery is becoming more and more widespread. The accurate puncture of breast cancer needs to solve the problems of tissue deformation and target displacement. In this study, we analysed the process of leech blood absorption and developed a robotic needle insertion method based on bionic technology to improve the accuracy of breast cancer diagnosis and treatment. Among them, the design purpose of the sucker manipulator is to adjust and fix the breast tissue. We use uncalibrated visual servo to control soft tissue deformation. We compare the puncture effect of bionic needle puncture robot and common needle puncture on breast prosthesis and in vitro tissue. Experimental data shows that, compared with ordinary needle insertion, the robotic needle insertion method based on bionic technology greatly reduces the targeting error. This method is expected to provide a safe and effective alternative to traditional puncture for breast cancer diagnosis and treatment.

OC-0695: MRI-guided robotic needle insertion for prostate brachytherapy: proof of principle study in patients

OC-0695: MRI-guided robotic needle insertion for prostate brachytherapy: proof of principle study in patients

Adaptive energy shaping control of robotic needle insertion

This work studies the control of a pneumatic actuator for needle insertion in soft tissue without using axial rotation or additional needle supports. Employing a simplified rigid-link model description of an axial-symmetric tip needle supported at the base, two energy shaping controllers are proposed. The friction forces of the pneumatic actuator are compensated adaptively and the stability conditions for the closed-loop equilibrium are discussed. The controllers are compared by means of simulations and experiments on two different silicone rubber phantoms. The results indicate that the proposed controllers effectively compensate the actuator's friction, which is comparable to the insertion forces for the chosen pneumatic actuators. The first controller only depends on the actuator's position thus it achieves the prescribed insertion depth but results in a larger tip rotation and corresponding deflection. The second controller also accounts for the rotation of the needle tip on the bending plane, which can consequently be reduced by over 70% for this specific system. This is achieved by modulating the actuator force and, in case of harder phantoms, by automatically limiting the insertion depth.

Deep Learning Based Real-Time OCT Image Segmentation and Correction for Robotic Needle Insertion Systems

This article proposes deep learning based real-time optical coherence tomography (OCT) image segmentation and correction algorithm for vision-based robotic needle insertion systems that can be used in DALK (deep anterior lamellar keratoplasty) surgery. The proposed algorithm provides the position of the needle tip, the lower boundary of the tissue, and the marginal insertion depth solving traditional issues of OCT images like refractive error, optical noise from surgical tools, and the slow speed of volumetric scanning. Through the ex-vivo experiment using 10 porcine corneas, the performance of the proposed algorithm with a robotic system was verified. The segmentation errors were 7.4 μm for the upper boundary, 10.5 μm for the lower boundary, and 3.6 μm for the needle tip. The difference in needle slope between the outside and inside of the cornea was dramatically reduced from 5.87 degree to 0.78 degree. The frame rate of the OCT image was 9.7 Hz, and the time delay of the image processing algorithm was 542.6 ms for 10 images of 512 × 512 pixels. The results of the proposed algorithm were compared with those of the previous studies.

A CT-guided robotic needle puncture method for lung tumours with respiratory motion

PurposePercutaneous interventions rely on needle puncture to deliver medical devices into lesions. For lung tumours, respiratory motion makes effective puncture procedures difficult to achieve. To address this issue, a needle puncture method considering respiration is proposed to improve the accuracy of lung puncture. MethodsThe accuracy of puncture is ensured by visualization and needle guidance. Dynamic visualization of the respiratory motion is developed for needle path planning based on four-dimensional computed tomography (4DCT) images. The rendered image is synchronized with the actual breathing by using respiratory signals. A robotic needle insertion strategy for velocity adjustment based on these respiratory signals is designed to guide the needle towards the moving tumour. ResultsThe dynamic visualization was tested on multiple 4DCT datasets and achieved a frame rate of over 32 frames per second (FPS). A computer simulation was carried out to verify the feasibility of the needle insertion strategy. Needle puncture was performed on a phantom, and a mean accuracy of 1.34±0.18 mm was achieved. ConclusionsIn this paper, an efficient and robust method is proposed to improve the visualization and targeting of lung puncture, which reduces the impact of respiratory motion on the accuracy.

A Novel Sensing Method to Detect Tissue Boundaries During Robotic Needle Insertion Based on Laser Doppler Flowmetry

This study investigates the use of Laser Doppler Flowmetry (LDF) as a method to detect tissue transitions during robotic needle insertions. Insertions were performed in gelatin tissue phantoms with different optical and mechanical properties and into ex-vivo sheep brain. The effect of changing the optical properties of gelatin tissue phantoms was first investigated and it was shown that using gelatin concentration to modify the stiffness of samples was suitable. Needle insertion experiments were conducted into both one-layer and two-layer gelatin phantoms. In both cases, three stages could be observed in the perfusion values: tissue loading, rupture and tissue cutting. These were correlated to force values measured from the tip of the needle during insertion. The insertions into ex-vivo sheep brain also clearly showed the time of rupture in both force and perfusion values, demonstrating that tissue puncture can be detected using an LDF sensor at the tip of a needle.

Robotic CT-guided out-of-plane needle insertion: comparison of angle accuracy with manual insertion in phantom and measurement of distance accuracy in animals

ObjectivesTo evaluate the accuracy of robotic CT-guided out-of-plane needle insertion in phantom and animal experiments.MethodsA robotic system (Zerobot), developed at our institution, was used for needle insertion. In the phantom experiment, 12 robotic needle insertions into a phantom at various angles in the XY and YZ planes were performed, and the same insertions were manually performed freehand, as well as guided by a smartphone application (SmartPuncture). Angle errors were compared between the robotic and smartphone-guided manual insertions using Student’s t test. In the animal experiment, 6 robotic out-of-plane needle insertions toward targets of 1.0 mm in diameter placed in the kidneys and hip muscles of swine were performed, each with and without adjustment of needle orientation based on reconstructed CT images during insertion. Distance accuracy was calculated as the distance between the needle tip and the target center.ResultsIn the phantom experiment, the mean angle errors of the robotic, freehand manual, and smartphone-guided manual insertions were 0.4°, 7.0°, and 3.7° in the XY plane and 0.6°, 6.3°, and 0.6° in the YZ plane, respectively. Robotic insertions in the XY plane were significantly (p < 0.001) more accurate than smartphone-guided insertions. In the animal experiment, the overall mean distance accuracy of robotic insertions with and without adjustment of needle orientation was 2.5 mm and 5.0 mm, respectively.ConclusionRobotic CT-guided out-of-plane needle insertions were more accurate than smartphone-guided manual insertions in the phantom and were also accurate in the in vivo procedure, particularly with adjustment during insertion.Key Points• Out-of-plane needle insertions performed using our robot were more accurate than smartphone-guided manual insertions in the phantom experiment and were also accurate in the in vivo procedure.• In the phantom experiment, the mean angle errors of the robotic and smartphone-guided manual out-of-plane needle insertions were 0.4° and 3.7° in the XY plane (p < 0.001) and 0.6° and 0.6° in the YZ plane (p = 0.65), respectively.• In the animal experiment, the overall mean distance accuracies of the robotic out-of-plane needle insertions with and without adjustments of needle orientation during insertion were 2.5 mm and 5.0 mm, respectively.