Magnetic resonance imaging (MRI)-guided focused ultrasound (MRg-FUS) is an effectivenoninvasiveintervention. However, when extending to mild hyperthermia treatment (mHT), accurate temperature control with uniform thermal distribution remains challenging for deep-seated targets in highly heterogeneous tissues. To this end, we propose a novel thermal modeling andclosed-loopcontrol scheme for robot-assisted MRg-FUS mHT. A discrete-time positive system is introduced for thermal dynamics modeling. By introducing an event-triggered model predictive controller, objective functions are formulated to optimize transducerphasesequences. Thermal feedback is leveraged to accommodate modeling uncertainties,closingthe control loop to generate constructive ultrasound interference. Our scheme enables simultaneous spatial and temporal heating control, maintaining target temperature within a narrow range ($41\,^\circ \mathrm{C}\text{--}43\,^\circ \mathrm{C}$). The thermal dose evaluated under unknown disturbances demonstrates the robustness of the proposed scheme against severe tissue heterogeneity. Overheating can be avoided, enhancing the potential for safe intervention. Results from mechanical transducer adjustment further support the feasibility of treating large target areas in robot-assisted mHT.

Robotic platforms provide consistent and precise tool positioning that significantly enhances retinal microsurgery. Integrating such systems with intraoperative optical coherence tomography (iOCT) enables image-guided robotic interventions, allowing autonomous performance of advanced treatments, such as injecting therapeutic agents into the subretinal space. However, tissue deformations due to tool-tissue interactions constitute a significant challenge in autonomous iOCT-guided robotic subretinal injections. Such interactions impact correct needle positioning and procedure outcomes. This paper presents a novel method for autonomous subretinal injection under iOCT guidance that considers tissue deformations during the insertion procedure. The technique is achieved through real-time segmentation and 3D reconstruction of the surgical scene from densely sampled iOCT B-scans, which we refer to as B5_ scans. Using B5-scans we monitor the position of the instrument relative to a virtual target layer between the ILM and RPE. Our experiments on ex-vivo porcine eyes demonstrate dynamic adjustment of the insertion depth and overall improved accuracy in needle positioning compared to prior autonomous insertion approaches. Compared to a 35% success rate in subretinal bleb generation with previous approaches, our method reliably created subretinal blebs in 90% our experiments. The source code and data used in this study are publicly available on GitHub11https://github.com/demirarikan/virtual-Iayer-retinal-surgery.

Retinal vein cannulation (RVC) is a minimally invasive microsurgical procedure for treating retinal vein occlusion (RVO), a leading cause of vision impairment. However, the small size and fragility of retinal veins, coupled with the need for high-precision, tremor-free needle manipulation, create significant technical challenges. These limitations highlight the need for robotic assistance to improve accuracy and stability. This study presents an automated robotic system with a top-down microscope and B-scan optical coherence tomography (OCT) imaging for precise depth sensing. Deep learning-based models enable real-time needle navigation, contact detection, and vein puncture recognition, using a chicken embryo model as a surrogate for human retinal veins. The system autonomously detects needle position and puncture events with 85% accuracy. The experiments demonstrate notable reductions in navigation and puncture times compared to manual methods. Our results demonstrate the potential of integrating advanced imaging and deep learning to automate microsurgical tasks, providing a pathway for safer and more reliable RVC procedures with enhanced precision and reproducibility.

Performing retinal vein cannulation (RVC) as a potential treatment for retinal vein occlusion (RVO) without the assistance of a surgical robotic system is very challenging to do safely. The main limitation is the physiological hand tremor of surgeons. Robot-assisted eye surgery technology may resolve the problems of hand tremors and fatigue and improve the safety and precision of RVC. The Steady-Hand Eye Robot (SHER) is an admittance-based robotic system that can filter out hand tremors and enables ophthalmologists to manipulate a surgical instrument inside the eye cooperatively. However, the admittance-based cooperative control mode does not safely minimize the contact force between the surgical instrument and the sclera to prevent tissue damage. In addition, features such as haptic feedback or hand motion scaling, which can improve the safety and precision of surgery, require a teleoperation control framework. This work presents, for the first time in the field of robot-assisted retinal microsurgery research, a registration-free bimanual adaptive teleoperation (BMAT) control framework using SHER 2.0 and SHER 2.1 robotic systems. Both SHERs are integrated with an adaptive force control (AFC) algorithm that dynamically and automatically minimizes the tool-sclera interaction forces, enforcing them within a safe limit. The scleral forces are measured using two fiber Bragg grating (FBG)-based force-sensing tools. The performance of the proposed BMAT control framework is evaluated by comparison with a bimanual adaptive cooperative (BMAC) framework in a vessel-following experiment conducted under a surgical microscope. Experimental results demonstrate the effectiveness of the BMAT control framework in performing a safe bimanual telemanipulation of the eye without over-stretching it, even in the absence of registration between the two robots.

Retinal surgery is a complex medical procedure that requires high precision dexterity to perform delicate instrument maneuvers with sub-millimeter accuracy. Minimizing the manual tremor and achieving precise and repeatable execution of surgical tasks has motivated the development of robotic platforms to overcome the limitations of manual surgery. However, specific tasks, such as instrument insertion through the trocar, are more challenging in robotic surgery than in conventional manual procedures since the robot control is often optimized for navigation inside the eye. This challenges the integration of robotic systems, creating a high cognitive load on the operator and prolonging the surgery time. Moreover, misalignment of the robot’s remote center of motion (RCM) and trocar position during the procedure can lead to excessive forces between the instrument and the trocar, potentially causing patient trauma. Precise and rapid localization of the trocars enables the automation of the insertion procedure and dynamic compensation of eye motion.In this work, we present a real-time marker-less method for 3D pose tracking of trocar, achieved with only a single monocular camera. Our experiments show promising results towards real-time trocar pose estimation and tracking, achieving an average error of 3◦in trocar orientation estimation, with an average processing time of 15 fps. This could serve as a foundation to improve robotic systems’ automation, integration, and efficiency of robotic systems for retinal surgery. The dataset created for this work is made publicly available.

A surgeon's physiological hand tremor can significantly impact the outcome of delicate and precise retinal surgery, such as retinal vein cannulation (RVC) and epiretinal membrane peeling. Robot-assisted eye surgery technology provides ophthalmologists with advanced capabilities such as hand tremor cancellation, hand motion scaling, and safety constraints that enable them to perform these otherwise challenging and high-risk surgeries with high precision and safety. Steady-Hand Eye Robot (SHER) with cooperative control mode can filter out surgeon's hand tremor, yet another important safety feature, that is, minimizing the contact force between the surgical instrument and sclera surface for avoiding tissue damage cannot be met in this control mode. Also, other capabilities, such as hand motion scaling and haptic feedback, require a teleoperation control framework. In this work, for the first time, we implemented a teleoperation control mode incorporated with an adaptive sclera force control algorithm using a PHANTOM Omni haptic device and a force-sensing surgical instrument equipped with Fiber Bragg Grating (FBG) sensors attached to the SHER 2.1 end-effector. This adaptive sclera force control algorithm allows the robot to dynamically minimize the tool-sclera contact force. Moreover, for the first time, we compared the performance of the proposed adaptive teleoperation mode with the cooperative mode by conducting a vessel-following experiment inside an eye phantom under a microscope.

In minimally invasive procedures such as biopsies and prostate cancer brachytherapy, accurate needle placement remains challenging due to limitations in current tracking methods related to interference, reliability, resolution or image contrast. This often leads to frequent needle adjustments and reinsertions. To address these shortcomings, we introduce an optimized needle shape-sensing method using a fully distributed grating-based sensor. The proposed method uses simple trigonometric and geometric modeling of the fiber using optical frequency domain reflectometry (OFDR), without requiring prior knowledge of tissue properties or needle deflection shape and amplitude. Our optimization process includes a reproducible calibration process and a novel tip curvature compensation method. We validate our approach through experiments in artificial isotropic and inhomogeneous animal tissues, establishing ground truth using 3D stereo vision and cone beam computed tomography (CBCT) acquisitions, respectively. Our results yield an average RMSE ranging from 0.58 ± 0.21 mm to 0.66 ± 0.20 mm depending on the chosen spatial resolution, achieving the submillimeter accuracy required for interventional procedures.

Recent advancements in age-related macular degeneration treatments necessitate precision delivery into the subretinal space, emphasizing minimally invasive procedures targeting the retinal pigment epithelium (RPE)-Bruch's membrane complex without causing trauma. Even for skilled surgeons, the inherent hand tremors during manual surgery can jeopardize the safety of these critical interventions. This has fostered the evolution of robotic systems designed to prevent such tremors. These robots are enhanced by FBG sensors, which sense the small force interactions between the surgical instruments and retinal tissue. To enable the community to design algorithms taking advantage of such force feedback data, this paper focuses on the need to provide a specialized dataset, integrating optical coherence tomography (OCT) imaging together with the aforementioned force data. We introduce a unique dataset, integrating force sensing data synchronized with OCT B-scan images, derived from a sophisticated setup involving robotic assistance and OCT integrated microscopes. Furthermore, we present a neural network model for image-based force estimation to demonstrate the dataset's applicability.

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.

In diagnosing and treating prostate cancer the flexible bevel tip needle insertion surgical technique is commonly used. Bevel tip needles experience asymmetric loading on the needle's tip, inducing natural bending of the needle and enabling control mechanisms for precise placement of the needle during surgery. Several methods leverage the needles natural bending to provide autonomous control of needle insertion for accurate needle placement in an effort to reduce excess tissue damage and improve patient outcomes from needle insertion intraventions. Moreover, control methods using lateral deflection of the needle intra-operatively to steer the needle during insertion have been studied and have shown promising results. Thus, to enable these autonomous control methods, real-time, intra-operative shape-sensing feedback is pivotal for optimal performance of the needle insertion control. This work presents an extension of our proven Lie-group theoretic shape-sensing model to handle lateral deflection of the needle during needle insertion and validate this extension with robotic needle insertions in phantom tissue using stereo vision as a ground truth. Furthermore, the system configuration for real-time shape-sensing is implemented using ROS 2, demonstrating average feedback frequency of 15 ± 8 Hz. Average needle shape errors realized from this extension under 1 mm, validating the shape-sensing models' extension.

In the last decade, various robotic platforms have been introduced that could support delicate retinal surgeries. Concurrently, to provide semantic understanding of the surgical area, recent advances have enabled microscope-integrated intraoperative Optical Coherent Tomography (iOCT) with high-resolution 3D imaging at near video rate. The combination of robotics and semantic understanding enables task autonomy in robotic retinal surgery, such as for subretinal injection. This procedure requires precise needle insertion for best treatment outcomes. However, merging robotic systems with iOCT intro-duces new challenges. These include, but are not limited to high demands on data processing rates and dynamic registration of these systems during the procedure. In this work, we propose a framework for autonomous robotic navigation for subretinal injection, based on intelligent real-time processing of iOCT volumes. Our method consists of an instrument pose estimation method, an online registration between the robotic and the iOCT system, and trajectory planning tailored for navigation to an injection target. We also introduce intelligent virtual B-scans, a volume slicing approach for rapid instrument pose estimation, which is enabled by Convolutional Neural Networks (CNNs). Our experiments on ex-vivo porcine eyes demonstrate the precision and repeatability of the method. Finally, we discuss identified challenges in this work and suggest potential solutions to further the development of such systems.

Important challenges in retinal microsurgery in-clude prolonged operating time, inadequate force feedback, and poor depth perception due to a constrained top-down view of the surgery. The introduction of robot-assisted technology could potentially deal with such challenges and improve the surgeon's performance. Motivated by such challenges, this work develops a strategy for autonomous needle navigation in retinal microsurgery aiming to achieve precise manipulation, reduced end-to-end surgery time, and enhanced safety. This is accomplished through real-time geometry estimation and chance-constrained Model Predictive Control (MPC) resulting in high positional accuracy while keeping scleral forces within a safe level. The robotic system is validated using both open-sky and intact (with lens and partial vitreous removal) ex vivo porcine eyes. The experimental results demonstrate that the generation of safe control trajectories is robust to small motions associated with head drift. The mean navigation time and scleral force for MPC navigation experiments are 7.208 s and 11.97 mN, which can be considered efficient and well within acceptable safe limits. The resulting mean errors along lateral directions of the retina are below 0.06 mm, which is below the typical hand tremor amplitude in retinal microsurgery.

Studies of the human brain during natural activities, such as locomotion, would benefit from the ability to image deep brain structures during these activities. While Positron Emission Tomography (PET) can image these structures, the bulk and weight of current scanners are not compatible with the desire for a wearable device. This has motivated the design of a robotic system to support a PET imaging system around the subject's head and to move the system to accommodate natural motion. We report here the design and experimental evaluation of a prototype robotic system that senses motion of a subject's head, using parallel string encoders connected between the robot-supported imaging ring and a helmet worn by the subject. This measurement is used to robotically move the imaging ring (coarse motion correction) and to compensate for residual motion during image reconstruction (fine motion correction). Minimization of latency and measurement error are the key design goals, respectively, for coarse and fine motion correction. The system is evaluated using recorded human head motions during locomotion, with a mock imaging system consisting of lasers and cameras, and is shown to provide an overall system latency of about 80 ms, which is sufficient for coarse motion correction and collision avoidance, as well as a measurement accuracy of about 0.5 mm for fine motion correction.

Cooperative robots for intraocular surgery allow surgeons to perform vitreoretinal surgery with high precision and stability. Several robot structural designs have shown capabilities to perform these surgeries. This research investigates the comparative performance of a serial and parallel cooperative-controlled robot in completing a retinal vessel-following task, with a focus on human-robot interaction performance and user experience. Our results indicate that despite differences in robot structure and interaction forces and torques, the two robots exhibited similar levels of performance in terms of general robot-to-patient interaction and average operating time. These findings have implications for the development and implementation of surgical robotics, suggesting that both serial and parallel cooperative-controlled robots can be effective for vitreoretinal surgery tasks.

Notable challenges during retinal surgery lend themselves to robotic assistance which has proven beneficial in providing a safe steady-hand manipulation. Efficient assistance from the robots heavily relies on accurate sensing of surgery states (e.g. instrument tip localization and tool-to-tissue interaction forces). Many of the existing tool tip localization methods require preoperative frame registrations or instrument calibrations. In this study using an iterative approach and by combining vision and force-based methods, we develop calibration- and registration-independent (RI) algorithms to provide online estimates of instrument stiffness (least squares and adaptive). The estimations are then combined with a state-space model based on the forward kinematics (FWK) of the Steady-Hand Eye Robot (SHER) and Fiber Brag Grating (FBG) sensor measurements. This is accomplished using a Kalman Filtering (KF) approach to improve the deflected instrument tip position estimations during robot-assisted eye surgery. The conducted experiments demonstrate that when the online RI stiffness estimations are used, the instrument tip localization results surpass those obtained from pre-operative offline calibrations for stiffness.

Subretinal injection (SI) is an ophthalmic surgical procedure that allows for the direct injection of therapeutic substances into the subretinal space to treat vitreoretinal disorders. Although this treatment has grown in popularity, various factors contribute to its difficulty. These include the retina’s fragile, nonregenerative tissue, as well as hand tremor and poor visual depth perception. In this context, the usage of robotic devices may reduce hand tremors and facilitate gradual and controlled SI. For the robot to successfully move to the target area, it needs to understand the spatial relationship between the attached needle and the tissue. The development of optical coherence tomography (OCT) imaging has resulted in a substantial advancement in visualizing retinal structures at micron resolution. This paper introduces a novel foundation for an OCT-guided robotic steering framework that enables a surgeon to plan and select targets within the OCT volume. At the same time, the robot automatically executes the trajectories necessary to achieve the selected targets. Our contribution consists of a novel combination of existing methods, creating an intraoperative OCT-Robot registration pipeline. We combined straightforward affine transformation computations with robot kinematics and a deep neural network-determined tool-tip location in OCT. We evaluate our framework’s capability in a cadaveric pig eye open-sky procedure and using an aluminum target board. Targeting the subretinal space of the pig eye produced encouraging results with a mean Euclidean error of 23.8μm.

Retinal surgery is a complex medical procedure that requires exceptional expertise and dexterity. For this purpose, several robotic platforms are currently under development to enable or improve the outcome of microsurgical tasks. Since the control of such robots is often designed for navigation inside the eye in proximity to the retina, successful trocar docking and insertion of the instrument into the eye represents an additional cognitive effort, and is therefore one of the open challenges in robotic retinal surgery. For this purpose, we present a platform for autonomous trocar docking that combines computer vision and a robotic setup. Inspired by the Cuban Colibri (hummingbird) aligning its beak to a flower using only vision, we mount a camera onto the endeffector of a robotic system. By estimating the position and pose of the trocar, the robot is able to autonomously align and navigate the instrument towards the Trocar Entry Point (TEP) and finally perform the insertion. Our experiments show that the proposed method is able to accurately estimate the position and pose of the trocar and achieve repeatable autonomous docking. The aim of this work is to reduce the complexity of the robotic setup prior to the surgical task and therefore, increase the intuitiveness of the system integration into clinical workflow.

A substantially advance skill-set is a prerequisite in the domain of retinal surgery, given that the surgical instruments, constrained by small incisions made on the sclera, should be manipulated in a confined intraocular space. Therefore, robotic technologies with a snake-like architecture may be critical in retinal surgery to overcome this problem. These robots are expected to approach a target on the retina from a suitable direction when accessing its anterior portion for procedures such as vein cannulation or membrane peeling. Typical end-effectors or tools for retinal surgery include needles, light pipes, pipettes, and grippers. However, there are no retinal surgery robots or devices equipped with enough bending and grasping functionalities. We developed an Improved Integrated Robotic Intraocular Snake (I2RIS) in previous works. I2RIS has a user interface (a tactile switch or joystick unit) to provide maneuverability to the snake-like distal end. This study presents a new microgripper with its drive mechanism and an interface for retinal surgery; this microgripper is implemented into I2RIS. The proposed microgripper has a simple mechanism; it comprised only three parts, including a nitinol drive wire that functions as a flexible hinge. The microgripper diameter is 0.9 mm, and the length is 2.6 mm. A real-size prototype model was used to demonstrate the effectiveness of the proposed microgripper. In addition, a pick-and-place task using an eye model was performed by I2RIS with the proposed microgripper. It is trusted that the utility of this microgripper can extend beyond retinal surgery into other microsurgical applications.

Complex needle shape prediction remains an issue for planning of surgical interventions of flexible needles. In this paper, we validate a theoretical method for flexible needle shape prediction allowing for non-uniform curvatures, extending upon a previous sensor-based model which 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 effectiveness in single-layer isotropic tissue for shape sensing and shape prediction capabilities. Experiments on a four-active area, FBG-sensorized needle were performed in varying single-layer isotropic tissues under stereo vision to provide 3D ground truth of the needle shape. The results validate a viable 3D needle shape prediction model accounting for non-uniform curvatures in flexible needles with mean needle shape sensing and prediction root-mean-square errors of 0.479 mm and 0.892 mm, respectively.

Intraocular surgery, one of the most challenging discipline of microsurgery, requires sensory and motor skills at the limits of human physiological capabilities combined with tremendously difficult requirements for accuracy and steadiness. Nowadays, robotics combined with advanced imaging has opened conspicuous and significant directions in advancing the field of intraocular microsurgery. Having patient treatment with greater safety and efficiency as the final goal, similar to other medical applications, robotics has a real potential to fundamentally change microsurgery by combining human strengths with computer and sensor-based technology in an information-driven environment. Still in its early stages, robotic assistance for intraocular microsurgery has been accepted with precaution in the operating room and successfully tested in a limited number of clinical trials. However, owing to its demonstrated capabilities including hand tremor reduction, haptic feedback, steadiness, enhanced dexterity, micrometer-scale accuracy, and others, microsurgery robotics has evolved as a very promising trend in advancing retinal surgery. This paper will analyze the advances in retinal robotic microsurgery, its current drawbacks and limitations, as well as the possible new directions to expand retinal microsurgery to techniques currently beyond human boundaries or infeasible without robotics.

Magnetic resonance imaging (MRI) can provide high-quality 3-D visualization of target anatomy, surrounding tissue, and instrumentation, but there are significant challenges in harnessing it for effectively guiding interventional procedures. Challenges include the strong static magnetic field, rapidly switching magnetic field gradients, high-power radio frequency pulses, sensitivity to electrical noise, and constrained space to operate within the bore of the scanner. MRI has a number of advantages over other medical imaging modalities, including no ionizing radiation, excellent soft-tissue contrast that allows for visualization of tumors and other features that are not readily visible by other modalities, true 3-D imaging capabilities, including the ability to image arbitrary scan plane geometry or perform volumetric imaging, and capability for multimodality sensing, including diffusion, dynamic contrast, blood flow, blood oxygenation, temperature, and tracking of biomarkers. The use of robotic assistants within the MRI bore, alongside the patient during imaging, enables intraoperative MR imaging (iMRI) to guide a surgical intervention in a closed-loop fashion that can include tracking of tissue deformation and target motion, localization of instrumentation, and monitoring of therapy delivery. With the ever-expanding clinical use of MRI, MRI-compatible robotic systems have been heralded as a new approach to assist interventional procedures to allow physicians to treat patients more accurately and effectively. Deploying robotic systems inside the bore synergizes the visual capability of MRI and the manipulation capability of robotic assistance, resulting in a closed-loop surgery architecture. This article details the challenges and history of robotic systems intended to operate in an MRI environment and outlines promising clinical applications and associated state-of-the-art MRI-compatible robotic systems and technology for making this possible.

This article presents a dexterous robotic system for autonomous debridement of osteolytic bone lesions in confined spaces. The proposed system is distinguished from the state-of-the-art orthopedics systems because it combines a rigid-link robot with a continuum manipulator (CM) that enhances reach in difficult-to-access spaces often encountered in surgery. The CM is equipped with flexible debriding instruments and fiber Bragg grating sensors. The surgeon plans on the patient's preoperative computed tomography and the robotic system performs the task autonomously under the surgeon's supervision. An optimization-based controller generates control commands on the fly to execute the task while satisfying physical and safety constraints. The system design and controller are discussed and extensive simulation, phantom and human cadaver experiments are carried out to evaluate the performance, workspace, and dexterity in confined spaces. Mean and standard deviation of target placement are 0.5 and 0.18 mm, and the robotic system covers 91% of the workspace behind an acetabular implant in treatment of hip osteolysis, compared to the 54% that is achieved by conventional rigid tools.

Retinal surgeons are required to manipulate multiple surgical instruments in a confined intraocular space, while the instruments are constrained at the small incisions made on the sclera. Furthermore, physiological hand tremor can affect the precision of the instrument motion. The Steady-Hand Eye Robot (SHER), developed in our previous study, enables tremor-free tool manipulation by employing a cooperative control scheme whereby the surgeon and robot can co-manipulate the surgical instruments. Although SHER enables precise and tremor-free manipulation of surgical tools, its straight and rigid structure imposes certain limitations, as it can only approach a target on the retina from one direction. As a result, the instrument could potentially collide with the eye lens when attempting to access the anterior portion of the retina. In addition, it can be difficult to approach a target on the retina from a suitable direction when accessing its anterior portion for procedures such as vein cannulation or membrane peeling. Snake-like robots offer greater dexterity and allow access to a target on the retina from suitable directions, depending on the clinical task at hand. In this study, we present an integrated, high-dexterity, cooperative robotic assistant for intraocular micromanipulation. This robotic assistant comprises an improved integrated robotic intraocular snake (I2RIS) with a user interface (a tactile switch or joystick unit) for the manipulation of the snake-like distal end and the SHER, with a detachable end-effector to which the I2RIS can be attached. The integrated system was evaluated through a set of experiments wherein subjects were requested to touch or insert into randomly-assigned targets. The results indicate that the high-dexterity robotic assistant can touch or insert the tip into the same target from multiple directions, with no significant increase in task completion time for either user interface.

Malaria is a worldwide scourge, and the broad deployment of an effective vaccine would improve the lives of millions of people. A vaccine based on Plasmodium falciparum (PfSPZ) sporozoites extracted from the salivary glands of infected mosquitoes shows significant promise. However, the large-scale industrial production of PfSPZ-based vaccines will benefit from automation of the key step of extracting sporozoites from mosquito salivary glands that is currently performed by manual microdissection. In this work, we demonstrate a robotic system prototype for extracting salivary glands from mosquitoes to streamline vaccine production and reduce the need for operators. In the proposed system, mosquitoes are decapitated in an automated robotic pick-place-decapitate process, then a squeezer apparatus extracts mosquito salivary glands from the body. Mosquito detection and body part localization are performed by computer vision methods. The software allows system operation in simulation and on the robotic hardware, which facilitates subsystem development and integration. Experiments show encouraging results with success rates of 93% in robotic mosquito manipulation and 87.1% in salivary gland extraction. The system has the potential to improve the efficiency of PfSPZ vaccine production with significant gains in throughput and reduction in training times for a highly deskilled initial manual step. Further, this system is expected to pave the way for a more mature future system.

Lumbar injection is an image-guided procedure performed manually for diagnosis and treatment of lower back pain and leg pain. Previously, we have developed and verified an MR-Conditional robotic solution to assisting the needle insertion process. Drawing on our clinical experiences, a virtual remote center of motion (RCM) constraint is implemented to enable our robot to mimic a clinician’s hand motion to adjust the needle tip position in situ. Force and image data are collected to study the needle behavior in gel phantoms during this motion, and a mechanics-based needle-tissue interaction model is proposed and evaluated to further examine the underlying physics. This work extends the commonly-adopted notion of an RCM for flexible needles, and introduces new motion parameters to describe the needle behavior. The model parameters can be tuned to match the experimental result to sub-millimeter accuracy, and this proposed needle manipulation method presents a safer alternative to laterally translating the needle during in situ needle adjustments.

The treatment of malaria is a global health challenge that stands to benefit from the widespread introduction of a vaccine for the disease. A method has been developed to create a live organism vaccine using the sporozoites (SPZ) of the parasite Plasmodium falciparum (Pf), which are concentrated in the salivary glands of infected mosquitoes. Current manual dissection methods to obtain these PfSPZ are not optimally efficient for large-scale vaccine production. We propose an improved dissection procedure and a mechanical fixture that increases the rate of mosquito dissection and helps to deskill this stage of the production process. We further demonstrate the automation of a key step in this production process, the picking and placing of mosquitoes from a staging apparatus into a dissection assembly. This unit test of a robotic mosquito pick-and-place system is performed using a custom-designed micro-gripper attached to a four degree of freedom (4-DOF) robot under the guidance of a computer vision system. Mosquitoes are autonomously grasped and pulled to a pair of notched dissection blades to remove the head of the mosquito, allowing access to the salivary glands. Placement into these blades is adapted based on output from computer vision to accommodate for the unique anatomy and orientation of each grasped mosquito. In this pilot test of the system on 50 mosquitoes, we demonstrate a 100% grasping accuracy and a 90% accuracy in placing the mosquito with its neck within the blade notches such that the head can be removed. This is a promising result for this difficult and non-standard pick-and-place task.

Retinal vein cannulation is a promising approach for treating retinal vein occlusion that involves injecting medicine into the occluded vessel to dissolve the clot. The approach remains largely unexploited clinically due to surgeon limitations in detecting interaction forces between surgical tools and retinal tissue. In this paper, a dual force constraint controller for robot-assisted retinal surgery was presented to keep the tool-to-vessel forces and tool-to-sclera forces below prescribed thresholds. A cannulation tool and forceps with dual force-sensing capability were developed and used to measure force information fed into the robot controller, which was implemented on existing Steady Hand Eye Robot platforms. The robotic system facilitates retinal vein cannulation by allowing a user to grasp the target vessel with the forceps and then enter the vessel with the cannula. The system was evaluated on an eye phantom. The results showed that, while the eyeball was subjected to rotational disturbances, the proposed controller actuates the robotic manipulators to maintain the average tool-to-vessel force at 10.9 mN and 13.1 mN and the average tool-to-sclera force at 38.1 mN and 41.2 mN for the cannula and the forcpes, respectively. Such small tool-to-tissue forces are acceptable to avoid retinal tissue injury. Additionally, two clinicians participated in a preliminary user study of the bimanual cannulation demonstrating that the operation time and tool-to-tissue forces are significantly decreased when using the bimanual robotic system as compared to freehand performance.

A fundamental challenge in retinal surgery is safely navigating a surgical tool to a desired goal position on the retinal surface while avoiding damage to surrounding tissues, a procedure that typically requires tens-of-microns accuracy. In practice, the surgeon relies on depth-estimation skills to localize the tool-tip with respect to the retina in order to perform the tool-navigation task, which can be prone to human error. To alleviate such uncertainty, prior work has introduced ways to assist the surgeon by estimating the tooltip distance to the retina and providing haptic or auditory feedback. However, automating the tool-navigation task itself remains unsolved and largely unexplored. Such a capability, if reliably automated, could serve as a building block to streamline complex procedures and reduce the chance for tissue damage. Towards this end, we propose to automate the tool-navigation task by learning to mimic expert demonstrations of the task. Specifically, a deep network is trained to imitate expert trajectories toward various locations on the retina based on recorded visual servoing to a given goal specified by the user. The proposed autonomous navigation system is evaluated in simulation and in physical experiments using a silicone eye phantom. We show that the network can reliably navigate a needle surgical tool to various desired locations within 137 μm accuracy in physical experiments and 94 μm in simulation on average, and generalizes well to unseen situations such as in the presence of auxiliary surgical tools, variable eye backgrounds, and brightness conditions.

This paper reports the development of a fully actuated body-mounted robotic assistant for MRI-guided low back pain injection. The robot is designed with a 4-DOF needle alignment module and a 2-DOF remotely actuated needle driver module. The 6-DOF fully actuated robot can operate inside the scanner bore during imaging; hence, minimizing the need of moving the patient in or out of the scanner during the procedure, and thus potentially reducing the procedure time and streamlining the workflow. The robot is built with a lightweight and compact structure that can be attached directly to the patient's lower back using straps; therefore, attenuating the effect of patient motion by moving with the patient. The novel remote actuation design of the needle driver module with beaded chain transmission can reduce the weight and profile on the patient, as well as minimize the imaging degradation caused by the actuation electronics. The free space positioning accuracy of the system was evaluated with an optical tracking system, demonstrating the mean absolute errors (MAE) of the tip position to be 0.99±0.46 mm and orientation to be 0.99±0.65°. Qualitative imaging quality evaluation was performed on a human volunteer, revealing minimal visible image degradation that should not affect the procedure. The mounting stability of the system was assessed on a human volunteer, indicating the 3D position variation of target movement with respect to the robot frame to be less than 0.7 mm.

Flexible medical instruments, such as Continuum Dexterous Manipulators (CDM), constitute an important class of tools for minimally invasive surgery. Accurate CDM shape reconstruction during surgery is of great importance, yet a challenging task. Fiber Bragg grating (FBG) sensors have demonstrated great potential in shape sensing and consequently tip position estimation of CDMs. However, due to the limited number of sensing locations, these sensors can only accurately recover basic shapes, and become unreliable in the presence of obstacles or many inflection points such as s-bends. Optical Frequency Domain Reflectometry (OFDR), on the other hand, can achieve much higher spatial resolution, and can therefore accurately reconstruct more complex shapes. Additionally, Random Optical Gratings by Ultraviolet laser Exposure (ROGUEs) can be written in the fibers to increase signal to noise ratio of the sensors. In this comparison study, the tip position error is used as a metric to compare both FBG and OFDR shape reconstructions for a 35 mm long CDM developed for orthopedic surgeries, using a pair of stereo cameras as ground truth. Three sets of experiments were conducted to measure the accuracy of each technique in various surgical scenarios. The tip position error for the OFDR (and FBG) technique was found to be 0.32 (0.83) mm in free-bending environment, 0.41 (0.80) mm when interacting with obstacles, and 0.45 (2.27) mm in s-bending. Moreover, the maximum tip position error remains sub-millimeter for the OFDR reconstruction, while it reaches 3.40 mm for FBG reconstruction. These results propose a cost-effective, robust and more accurate alternative to FBG sensors for reconstructing complex CDM shapes.

Robot-assisted retinal surgery has become increasingly prevalent in recent years in part due to the potential for robots to help surgeons improve the safety of an immensely delicate and difficult set of tasks. The integration of robots into retinal surgery has resulted in diminished surgeon perception of tool-to-tissue interaction forces due to robot's stiffness. The tactile perception of these interaction forces (sclera force) has long been a crucial source of feedback for surgeons who rely on them to guide surgical maneuvers and to prevent damaging forces from being applied to the eye. This problem is exacerbated when there are unfavorable sclera forces originating from patient movements (dynamic eyeball manipulation) during surgery which may cause the sclera forces to increase even drastically. In this study we aim at evaluating the efficacy of providing warning auditory feedback based on the level of sclera force measured by force sensing instruments. The intent is to enhance safety during dynamic eye manipulations in robot-assisted retinal surgery. The disturbances caused by lateral movement of patient's head are simulated using a piezo-actuated linear stage. The Johns Hopkins Steady-Hand Eye Robot (SHER), is then used in a multi-user experiment. Twelve participants are asked to perform a mock retinal surgery by following painted vessels inside an eye phantom using a force sensing instrument while auditory feedback is provided. The results indicate that the users are able to handle the eye motion disturbances while maintaining the sclera forces within safe boundaries when audio feedback is provided.

Robot-assisted vitreoretinal surgery can filter surgeons' hand tremors and provide safe, accurate tool manipulation. In this paper, we report the design, optimization, and evaluation of a novel tilt mechanism for a new Steady-Hand Eye Robot (SHER). The new tilt mechanism features a four-bar linkage design and has a compact structure. Its kinematic configuration is optimized to minimize the required linear range of motion (LRM) for implementing a virtual remote center-of-motion (V-RCM) while tilting a surgical tool. Due to the different optimization constraints for the robots at the left and right sides of the human head, two configurations of this tilt mechanism are proposed. Experimental results show that the optimized tilt mechanism requires a significantly smaller LRM (e.g. 5.08 mm along Z direction and 8.77 mm along Y direction for left side robot) as compared to the slider-crank tilt mechanism used in the previous SHER (32.39 mm along Z direction and 21.10 mm along Y direction). The feasibility of the proposed tilt mechanism is verified in a mock bilateral robot-assisted vitreoretinal surgery. The ergonomically acceptable robot postures needed to access the surgical field is also determined.

In this article, we present a novel stochastic algorithm called simultaneous sensor calibration and deformation estimation (SCADE) to address the problem of modeling deformation behavior of a generic continuum manipulator (CM) in free and obstructed environments. In SCADE, using a novel mathematical formulation, we introduce a priori model-independent filtering algorithm to fuse the continuous and inaccurate measurements of an embedded sensor (e.g., magnetic or piezoelectric sensors) with an intermittent but accurate data of an external imaging system (e.g., optical trackers or cameras). The main motivation of this article is the crucial need of obtaining an accurate shape/position estimation of a CM utilized in a surgical intervention. In these robotic procedures, the CM is typically equipped with an embedded sensing unit (ESU) while an external imaging modality (e.g., ultrasound or a fluoroscopy machine) is also available in the surgical site. The results of two different set of prior experiments in free and obstructed environments were used to evaluate the efficacy of SCADE algorithm. The experiments were performed with a CM specifically designed for orthopaedic interventions equipped with an inaccurate Fiber Bragg Grating (FBG) ESU and overhead camera. The results demonstrated the successful performance of the SCADE algorithm in simultaneous estimation of unknown deformation behavior of the utilized unmodeled CM together with realizing the time-varying drift of the poor-calibrated FBG sensing unit. Moreover, the results showed the phenomenal out-performance of the SCADE algorithm in estimation of the CM's tip position as compared to FBG-based position estimations.

One of the significant challenges of moving from manual to robot-assisted retinal surgery is the loss of perception of forces applied to the sclera (sclera forces) by the surgical tools. This damping of force feedback is primarily due to the stiffness and inertia of the robot. The diminished perception of tool-to-eye interactions might put the eye tissue at high risk of injury due to excessive sclera forces or extreme insertion of the tool into the eye. In the present study therefore a 1-dimensional adaptive control method is customized for 3-dimensional control of sclera force components and tool insertion depth and then implemented on the velocity-controlled Johns Hopkins Steady-Hand Eye Robot. The control method enables the robot to perform autonomous motions to make the sclera force and/or insertion depth of the tool tip to follow pre-defined desired and safe trajectories when they exceed safe bounds. A robotic light pipe holding application in retinal surgery is also investigated using the adaptive control method. The implementation results indicate that the adaptive control is able to achieve the imposed safety margins and prevent sclera forces and insertion depth from exceeding safe boundaries.

Retinal microsurgery is technically demanding and requires high surgical skill with very little room for manipulation error. During surgery the tool needs to be inserted into the eyeball while maintaining constant contact with the sclera. Any unexpected manipulation could cause extreme tool-sclera contact force (scleral force) thus damage the sclera. The introduction of robotic assistance could enhance and expand the surgeon's manipulation capabilities during surgery. However, the potential intra-operative danger from surgeon's misoperations remains difficult to detect and prevent by existing robotic systems. Therefore, we propose a method to predict imminent unsafe manipulation in robot-assisted retinal surgery and generate feedback to the surgeon via auditory substitution. The surgeon could then react to the possible unsafe events in advance. This work specifically focuses on minimizing sclera damage using a force-sensing tool calibrated to measure small scleral forces. A recurrent neural network is designed and trained to predict the force safety status up to 500 milliseconds in the future. The system is implemented using an existing "steady hand" eye robot. A vessel following manipulation task is designed and performed on a dry eye phantom to emulate the retinal surgery and to analyze the proposed method. Finally, preliminary validation experiments are performed by five users, the results of which indicate that the proposed early warning system could help to reduce the number of unsafe manipulation events.

When robotic assistance is present into vitreoretinal surgery, the surgeon will experience reduced sensory input that is otherwise derived from the tool's interaction with the eye wall (sclera). We speculate that disconnecting the surgeon from this sensory input may increase the risk of injury to the eye and affect the surgeon's usual technique. On the other hand, robot autonomous motion to enhance patient safety might inhibit the surgeons tool manipulation and diminish surgeon comfort with the procedure. In this study, to investigate the parameters of patient safety and surgeon comfort in a robot-assisted eye surgery, we implemented three different approaches designed to keep the scleral force in a safe range during a synergic eye manipulation task. To assess the surgeon comfort during these procedures, the amount of interference with the surgeons usual maneuvers has been analyzed by defining quantitative comfort metrics. The first two utilized scleral force control approaches are based on an adaptive force control method in which the robot actively counteracts any excessive force on the sclera. The third control method is based on a virtual fixture approach in which a virtual wall is created for the surgeon in the unsafe directions of manipulation. The performance of the utilized approaches was evaluated in user studies with two experienced retinal surgeons and the outcomes of the procedure were assessed using the defined safety and comfort metrics. Results of these analyses indicate the significance of the opted control paradigm on the outcome of a safe and comfortable robot-assisted eye surgery.

In this paper, a novel semi-autonomous control framework is presented for enabling probe-based confocal laser endomicroscopy (pCLE) scan of the retinal tissue. With pCLE, retinal layers such as nerve fiber layer (NFL) and retinal ganglion cell (RGC) can be scanned and characterized in real-time for an improved diagnosis and surgical outcome prediction. However, the limited field of view of the pCLE system and the micron-scale optimal focus distance of the probe, which are in the order of physiological hand tremor, act as barriers to successful manual scan of retinal tissue. Therefore, a novel sensorless framework is proposed for real-time semi-autonomous endomicroscopy scanning during retinal surgery. The framework consists of the Steady-Hand Eye Robot (SHER) integrated with a pCLE system, where the motion of the probe is controlled semi-autonomously. Through a hybrid motion control strategy, the system autonomously controls the confocal probe to optimize the sharpness and quality of the pCLE images, while providing the surgeon with the ability to scan the tissue in a tremor-free manner. Effectiveness of the proposed architecture is validated through experimental evaluations as well as a user study involving 9 participants. It is shown through statistical analyses that the proposed framework can reduce the work load experienced by the users in a statistically-significant manner, while also enhancing their performance in retaining pCLE images with optimized quality.

Retinal surgery involves manipulating very delicate tissues within the confined area of eyeball. In such demanding practices, patient involuntary head movement might abruptly raise tool-to-eyeball interaction forces which would be detrimental to eye. This study is aimed at implementing different force control strategies and evaluating how they contribute to attaining sclera force safety while patient head drift is present. To simulate patient head movement, a piezoelectric-actuated linear stage is used to produce random motions in a single direction in random time intervals. Having an eye phantom attached to the linear stage then an experienced eye surgeon is asked to manipulate the eye and repeat a mock surgical task both with and without the assist of the Steady-Hand Eye Robot. For the freehand case, warning sounds were provided to the surgeon as auditory feedback to alert him about excessive slclra forces. For the robot-assisted experiments two variants of an adaptive sclera force control and a virtual fixture method were deployed to see how they can maintain eye safety under head drift circumstances. The results indicate that the developed robot control strategies are able to compensate for head drift and keep the sclera forces under safe levels as well as the free hand operation.

This paper presents a body-mounted, four degree-of-freedom (4-DOF) parallel mechanism robot for image-guided percutaneous interventions. The design of the robot is optimized to be light weight and compact such that it could be mounted to the patient body. It has a modular design that can be adopted for assisting various image-guided, needle-based percutaneous interventions such as arthrography, biopsy and brachytherapy seed placement. The robot mechanism and the control system are designed and manufactured with components compatible with imaging modalities including Magnetic Resonance Imaging (MRI) and Computed Tomography (CT). The current version of the robot presented in this paper is optimized for shoulder arthrography under MRI guidance; a Z-shaped fiducial frame is attached to the robot, providing accurate and repeatable robot registration with the MR scanner coordinate system. Here we present the mechanical design of the manipulator, robot kinematics, robot calibration procedure, and preliminary bench-top accuracy assessment. The bench-top accuracy evaluation of the robotic manipulator shows average translational error of 1.01 mm and 0.96 mm in X and Z axes, respectively, and average rotational error of 3.06 degrees and 2.07 degrees about the X and Z axes, respectively.

Tracking and controlling the shape of continuum dexterous manipulators (CDM) in constraint environments is a challenging task. The imposed constraints and interaction with unknown obstacles may conform the CDM's shape and therefore demands for shape sensing methods which do not rely on direct line of sight. To address these issues, we integrate a novel Fiber Bragg Grating (FBG) shape sensing unit into a CDM, reconstruct the shape in real-time, and develop an optimization-based control algorithm using FBG tip position feedback. The CDM is designed for less-invasive treatment of osteolysis (bone degradation). To evaluate the performance of the feedback control algorithm when the CDM interacts with obstacles, we perform a set of experiments similar to the real scenario of the CDM interaction with soft and hard lesions during the treatment of osteolysis. In addition, we propose methods for identification of the CDM collisions with soft or hard obstacles using the jacobian information. Results demonstrate successful control of the CDM tip based on the FBG feedback and indicate repeatability and robustness of the proposed method when interacting with unknown obstacles.

Retinal microsurgery is technically demanding and requires high surgical skill with very little room for manipulation error. The introduction of robotic assistance has the potential to enhance and expand a surgeon's manipulation capabilities during retinal surgery, i.e., improve precision, cancel physiological hand tremor, and provide sensing information. However, surgeon performance may also be negatively impacted by robotic assistance due to robot structural stiffness and nonintuitive controls. In complying with robotic constraints, the surgeon loses the dexterity of the human hand. In this paper, we present a preliminary experimental study to evaluate user behavior when affected by robotic assistance during mock retinal surgery. In these experiments user behavior is characterized by measuring the forces applied by the user to the sclera, the tool insertion/retraction speed, the tool insertion depth relative to the scleral entry point, and the duration of surgery. The users' behavior data is collected during three mock retinal surgery tasks with four users. Each task is conducted using both freehand and robot-assisted techniques. The univariate user behavior and the correlations of multiple parameters of user behavior are analyzed. The results show that robot assistance prolongs the duration of the surgery and increases the manipulation forces applied to sclera, but refines the insertion velocity and eliminates hand tremor.

Due to the confined intraocular space and physical constraints in tool manipulation, snake-like robots have a significant potential for use in retinal microsurgery. By enhancing the dexterity at the tool tip, not only the operable space on the retina can be enlarged, but also the delicate target tissues can be reached at an optimal angle minimizing the damage and making the operation much easier. In this study, we present an improved version of our earlier integrated intraocular snake (IRIS) robot, and combine it with another robotic assistant: the cooperatively controlled Steady-Hand Eye Robot (SHER). SHER is used to drive IRIS close to the retina with precision, while IRIS makes omnidirectional bends by combining its yaw and pitch motions and provides a significantly enhanced intraocular dexterity while holding the sclerotomy port fixed. For precise control of IRIS, its snake-like tip actuation has been characterized through experiments considering both a free tool tip and external loading at the tool tip. The workspace analysis showed ±45° yaw and pitch with excellent repeatability (±1°) despite the highly miniaturized articulated segment length (3 mm) and very thin shaft (Ø 0.9 mm). Our preliminary experiments in an artificial eye model have shown feasibility in reaching targets requiring bends up to 55° accurately.

The determination of a flexible needle shape during the insertion is an important issue in minimally invasive surgery techniques. This is especially critical when considering conventional surgery procedures where a surgeon usually determines how to proceed needle insertion further based on the current needle trajectory inserted into tissue during biopsy and removal of malignant tissues in the body. In this paper, we propose a new method to determine the shape of a needle that is being inserted, together with the curvature measurement data obtained by fiber Bragg gratings (FBG) sensors inside the needle. This approach can be particularly advantageous to the situations where visual guidance (such as by ultrasound probes) is not easily applicable. The description of a needle shape is based on the elastic rod theory and Lie-group-theoretic approach. We also present the comparison between two different calibration methods, which have an impact on the quality of the results of the proposed method. In order to verify the proposed method, needle trajectories by the model are compared with experimental data obtained by image analysis, which in turn emphasizes the capability of the proposed method.

This paper focuses on the design and quantitative characterization of a debriding tool integrated with a robotic system to treat osteolysis (bone degradation). Osteolysis typically occurs due to wear of the polyethylene liner of the acetabular implant after total hip replacement surgery. In less invasive treatment of osteolysis, surgeons conventionally use rigid tools to debride the lesion, however with these inflexible instruments, complex lesion shapes are not completely treatable (about 50%). To address this issue, we have developed a debriding tool that passes through the lumen of a continuum dexterous manipulator (CDM). Integration of the CDM with a robotic arm assists the surgeon to reach the desired region behind the implant. Performance of the debriding tool integrated with this system was quantitatively evaluated during a simulated robot-assisted lesion debriding scenario. Rotational speed, aspiration pressure and irrigation flow of the debriding tool, as well as the sweeping velocity of the robotic system were identified as effective parameters in this procedure. Results indicate that maximum efficiency of the tool is achievable in a particular combination of these parameters.

Lesions of the lateral skull base in the petrous apex present unique surgical challenges because of the proximity of critical structures, including the inner ear, carotid artery, jugular bulb, facial nerve, lower cranial nerves, dura and brain. Currently, there are few appropriate surgical devices that can reach and remove these lesions, each with their own disadvantages. Here we investigate the feasibility of a dexterous continuum manipulator (DCM) capable of C-& S-shaped bends enabling dissection with remote center of motion (RCM) deep to the intact inner ear. A dedicated borescope channel provides the necessary visualization, while a flexible ring curette pre-shaped with a nitinol strip is designed to work through the instrument lumen for curettage of a cystic lesion. The kinematics of the DCM with the ring curette subject to an RCM constraint are investigated to explore the boundaries of a typical cyst cavity. Experiments in the planar phantom are carried out to validate feasibility, and results show that the proposed solution is practicable, accomplishing 80% and 83% removal of cysts for two kinds of boundaries.

Retinal surgery is one of the most technically challenging surgical disciplines. Many robotic systems have been developed to enhance the surgical capabilities. However, very few of them provide the surgeon the dexterity within the patient's eye to enable more flexible, more advanced surgical procedures. This paper presents a sub-millimeter intraocular dexterous robot, the Integrated Robotic Intraocular Snake (IRIS). The variable neutral-line mechanism is used to provide very high dexterity with a very small form factor. The IRIS distal dexterous unit is 0.9 mm in diameter and about 3 mm in length. It enables two rotational degrees of freedom at the distal end of the ophthalmic instruments. The analysis on contact mechanics provides a reference for the adjustment of the wire pretension. Redundant actuation is implemented by using one motor for each wire. A motion scaling transmission is developed to overcome the suboptimal resolution of the motors. A scale-up model of the IRIS is built for initial experimental evaluation. Preliminary results show that the scale-up IRIS can provide large range of motion. For given bending angle, the kinematic model can estimate the desired wire translation when the friction is not significant. The first prototype of the actual-scale IRIS is assembled and tested.

Shape sensing techniques utilizing Fiber Bragg grating (FBG) arrays can enable real-time tracking and control of dexterous continuum manipulators (DCM) used in minimally invasive surgeries. For many surgical applications, the DCM may need to operate with much larger curvatures than what current shape sensing methods can detect. This paper proposes a novel shape sensor, which can detect a radius of curvature of 15 mm for a 35 mm long DCM. For this purpose, we used FBG sensors along with nitinol wires as the supporting substrates to form a triangular cross section. For verification, we assembled the sensor inside the wall of the DCM. Experimental results indicate that the proposed sensor can detect the DCM's curvature with an average error of 3.14%.

In retinal microsurgery, membrane peeling is a standard procedure requiring the delamination of a thin fibrous membrane adherent to the retina surface by applying very small forces. Robotic devices with combined force-sensing instruments have significant potential to assist this procedure by facilitating membrane delamination through induced micro-vibrations. However, defining the optimal frequency and amplitude for generating such vibrations, and updating these parameters during the procedure is not trivial. Automatic adjustment of these parameters via an adaptive control scheme is possible only if the individual parameter effects on delamination behavior are known. This study presents an experimental exploration of how micro-vibration amplitude and frequency affect membrane peeling forces alone. Combining a micromanipulator and a force-sensing micro-forceps, several peeling experiments were done on artificial phantoms (bandages) and inner shell membrane of raw chicken eggs. In the tested range of micro-vibration frequencies (10-50 Hz) the average delamination force was minimized mostly at 30 Hz for the bandages and at 50 Hz for the egg membranes. Increasing the micro-vibration amplitude from 50 μm up to 150 μm provided further reduction in average force, thus facilitated membrane delamination.

In vitreoretinal practice, controlled tremor-free motion and limitation of applied forces to the retina are two highly desired features. This study addresses both requirements with a new integrated system: a force-sensing motorized micro-forceps combined with an active tremor-canceling handheld micromanipulator, known as Micron. The micro-forceps is a 20 Ga instrument that is mechanically decoupled from its handle and senses the transverse forces at its tip with an accuracy of 0.3 mN. Membrane peeling trials on a bandage phantom revealed a 60-95% reduction in the 2-20 Hz band in both the tip force and position spectra, while peeling forces remained below the set safety threshold.

Robotic systems have the potential to assist vitreoretinal surgeons in extremely difficult surgical tasks inside the human eye. In addition to reducing hand tremor and improving tool positioning, a robotic assistant can provide assistive motion guidance using virtual fixtures, and incorporate real-time feedback from intraocular force sensing ophthalmic instruments to present tissue manipulation forces, that are otherwise physically imperceptible to the surgeon. This paper presents the design of an FBG-based, multi-function instrument that is capable of measuring mN-level forces at the instrument tip located inside the eye, and also the sclera contact location on the instrument shaft and the corresponding contact force. The given information is used to augment cooperatively controlled robot behavior with variable admittance control. This effectively creates an adaptive remote center-of-motion (RCM) constraint to minimize eye motion, but also allows the translation of the RCM location if the instrument is not near the retina. In addition, it provides force scaling for sclera force feedback. The calibration and validation of the multi-function force sensing instrument are presented, along with demonstration and performance assessment of the variable admittance robot control on an eye phantom.

This paper presents an implementation and evaluation of the Robotic ENT Microsurgery System (REMS). The implementation is discussed in reference to analysis from previous work, and evaluated using a simulated surgical task designed to resemble microlaryngeal phonosurgery. Preliminary technical evaluations of resolution and accuracy are also presented. The results of the evaluations reveal that the system statistically significantly improves surgical precision (p < 0.01) with only a small increase in operating time. The force data recorded also reveals that operating force can be significantly affected by ergonomic factors, and that warnings for excessive force and workspace limits are needed.

This paper undertakes the modular design and development of a minimally invasive surgical manipulator for MRI-guided transperineal prostate interventions. Severe constraints for the MRI-compatibility to hold the minimum artifact on the image quality and dimensions restraint of the bore scanner shadow the design procedure. Regarding the constructive design, the manipulator kinematics has been optimized and the effective analytical needle workspace is developed and followed by proposing the workflow for the manual needle insertion. A study of the finite element analysis is established and utilized to improve the mechanism weaknesses under some inevitable external forces to ensure the minimum structure deformation. The procedure for attaching a sterile plastic drape on the robot manipulator is discussed. The introduced robotic manipulator herein is aimed for the clinically prostate biopsy and brachytherapy applications.

In vitreoretinal surgery, application of excessive forces and unintentional motion due to hand-tremor can easily result in serious complications. Robotic assistance when combined with tool-to-tissue force sensing capabilities has significant potential to improve such practice. In this paper, we evaluate the membrane peeling performance of a single user for two distinct robotic systems with integrated force sensing capabilities: Micron and the Steady-Hand Robot. We show that these systems provide promising performance improvement with similar impact on peeling forces and comparable tremor cancellation trends.

Robotic assistants and smart surgical instruments have been developed to overcome many significant physiological limitations faced by vitreoretinal surgeons, one of which is lack of force perception below 7.5 mN. This paper reports the development of a new force sensor based on fiber Bragg grating (FBG) with the ability to sense forces at the tip of the surgical instrument located inside the eye and also provide information about instrument interaction with the sclera. The sclera section provides vital feedback for cooperative robot control to minimize potentially dangerous forces on the eye. Preliminary results with 2×2 degree-of-freedom (DOF) sensor and force scaling robot control demonstrate significant reduction of forces on the sclera. The design and analysis of the sensor is presented along with a simulated robot assisted retinal membrane peeling on a phantom with sclera constraints and audio feedback.

This paper reports the error specifications and error analysis for a new cooperatively controlled ENT microsurgery robot. The new robot is designed for three specific ENT surgeries, endonasal skull base surgery, transoral laryngeal surgery, and cochlear implant surgery. The design requirements for the robot resolution, accuracy, stiffness, and repeatability for these surgery types are discussed and analyzed, and the required robot parameters are calculated. The mechanical design of the robot is then analyzed and shown to fulfill these requirements in the case where it is in its home configuration. The analysis is then extended to cover the robot's whole workspace.

This study presents one of the enabling technologies for teleoperated bevel-tip needle steering under real-time MRI guidance i.e. capability of tracking the needle with higher accuracy and bandwidth than real-time MRI. Three fibers, each with three Fiber Bragg Gratings (FBG) were embedded into a 0.6 mm inner stylet of a 20G MRI-compatible biopsy needle. The axial force caused by the bevel-tip was considered in the analysis using beam-column theory. Since the insertion depth is varying, the minimum number of sensors and their optimal locations in the fibers were determined such that the tip position error estimation is below 0.5 mm for all insertion depths. A practical and accurate calibration method for the apparatus is presented. The instrumented needle was fabricated to fit in the needle driver unit of a MRI-compatible needle steering robot. The tracking apparatus was calibrated, including compensation for temperature changes in tissue during insertion. Experimental results showed needle tip tracking error below 0.5 mm at different insertion depths. Real-time 3D shape of the needle was visualized in 3D Slicer yielding navigation of the needle in real-time.

Image-guided radiation therapy (IGRT) involves two main procedures, performed in different rooms on different days: (1) treatment planning in the simulator room on the first day, and (2) radiotherapy in the linear accelerator room over multiple subsequent days. Both the simulator and the linear accelerator include CT imaging capabilities, which enables both treatment planning and reproducible patient setup, but does not provide good soft tissue contrast or allow monitoring of the target during treatment. We propose a cooperatively-controlled robot to reproducibly position an ultrasound (US) probe on the patient during simulation and treatment, thereby improving soft tissue visualization and allowing real-time monitoring of the target. A key goal of the robotic system is to produce consistent tissue deformations for both CT and US imaging, which simplifies registration of these two modalities. This paper presents the robotic system design and describes a novel control algorithm that employs virtual springs to implement guidance virtual fixtures during "hands on" cooperative control.

This paper reports the design requirements, practical challenges, and a preliminary design for a magnetic resonance imaging (MRI) guided, three degree-of-freedom (DOF) transrectal prostate intervention robot. We show the operational space constraints imposed by patient anatomy when performing transrectal prostate procedures in a magnetic resonance (MR) scanner bore, as determined by analyzing data from 12 patient procedures with a device. We also describe practical challenges arising in designing a compact actuated MR compatible needle placement robot for MRI-guided transrectal needle intervention in the prostate. We present a preliminary design which aims to improve upon previous un-actuated and partially-actuated devices with the addition of an actuated needle insertion module. Such an enhancement enables needle driving to take place inside the MR scanner bore and thereby may reduce the overall procedure time - thus improving patient comfort and reducing likelyhood of needle targeting errors resulting from patient motion. We show that it is feasible to add such actuation while reducing the footprint of the device in accordance with the anatomical and MR scanner constraints and practical design requirements.

Highly accurate positioning is fundamental to the performance of vitreoretinal microsurgery. Of vitreoretinal procedures, membrane peeling is among the most prone to complications since extremely delicate manipulation of retinal tissue is required. Associated tool-to-tissue interaction forces are usually below the threshold of human perception, and the surgical tools are moved very slowly, within the 0.1-0.5 mm/s range. During the procedure, unintentional tool motion and excessive forces can easily give rise to vision loss or irreversible damage to the retina. A successful surgery includes two key features: controlled tremor-free tool motion and control of applied force. In this study, we present the potential benefits of a micro-force sensing robot in vitreoretinal surgery. Our main contribution is implementing fiber Bragg grating based force sensing in an active tremor canceling handheld micromanipulator, known as Micron, to measure tool-to-tissue interaction forces in real time. Implemented auditory sensory substitution assists in reducing and limiting forces. In order to test the functionality and performance, the force sensing Micron was evaluated in peeling experiments with adhesive bandages and with the inner shell membrane from chicken eggs. Our findings show that the combination of active tremor canceling together with auditory sensory substitution is the most promising aid that keeps peeling forces below 7 mN with a significant reduction in 2-20 Hz oscillations.

Recently several systems for magnetic resonance image (MRI) guided needle placement in the prostate have been reported. In comparison to conventional ultrasound-guided needle placement in the prostate, these MRI-guided systems promise improved targeting accuracy for prostate intervention procedures including biopsy, fiducial marker insertion, injection and focal therapy. In MRI-guided needle interventions, after a needle is inserted, the needle position is often confirmed with a volumetric MRI scan. Commonly used titanium needles are not directly visible in an MR image, but they generate a susceptibility artifact in the immediate neighborhood of the needle. This paper reports the results of a quantitative study of the relation between the true position of titanium biopsy needle and the corresponding needle artifact position in MR images. The titanium needle artifact was found to be displaced 0.38 mm and 0.32 mm shift in scanner's frequency and phase encoding direction, respectively. The artifact at the tip of the titanium needle was observed to bend toward the scanner's B(0) magnetic field direction.

This paper reports the design, development, and magnetic resonance imaging (MRI) compatibility evaluation of an actuated transrectal prostate robot for MRI-guided intervention. The robot employs an actuated needle guide with the goal of reducing interventional procedure times and increasing needle placement accuracy. The design of the robot, employing piezo-ceramic-motor actuated needle guide positioning and manual needle insertion, is reported. Results of a MRI compatibility study show no reduction of MRI image signal-to-noise-ratio (SNR) with the motors disabled and a 40% to 60% reduction in SNR with the motors enabled. The addition of radio-frequency (RF) shielding is shown to significantly reduce image SNR degradation due to the presence of the robotic device.

The Small Animal Radiation Research Platform (SARRP) is a novel and complete system capable of delivering multidirectional (focal), kilo-voltage radiation fields to targets in small animals under robotic control using cone-beam CT (CBCT) image guidance. The capability of the SARRP to deliver highly focused beams to multiple animal models provides new research opportunities that more realistically bridge laboratory research and clinical translation. This paper describes the design and operation of the SARRP for precise radiation delivery. Different delivery procedures are presented which enable the system to radiate through a series of points, representative of a complex shape. A particularly interesting case is shell dose irradiation, where the goal is to deliver a high dose of radiation to the shape surface, with minimal dose to the shape interior. The ability to deliver a dose shell allows mechanistic research of how a tumor interacts with its microenvironment to sustain its growth and lead to its resistance or recurrence.

Magnetic Resonance Imaging (MRI) guided prostate biopsy and brachytherapy has been introduced in order to enhance the cancer detection and treatment. For the accurate needle positioning, a number of robotic assistants have been developed. However, problems exist due to the strong magnetic field and limited workspace. Pneumatically actuated robots have shown the minimum distraction in the environment but the confined workspace limits optimal robot design and thus controllability is often poor. To overcome the problem, a simple external damping mechanism using timing belts was sought and a 1-DOF mechanism test result indicated sufficient positioning accuracy. Based on the damping mechanism and modular system design approach, a new workspace-optimized 4-DOF parallel robot was developed for the MRI-guided prostate biopsy and brachytherapy. A preliminary evaluation of the robot was conducted using previously developed pneumatic controller and satisfying results were obtained.

This paper reports the development of novel micro-force sensing tools for retinal microsurgery. Retinal microsurgery requires extremely delicate manipulation of retinal tissue, and tool-to-tissue interaction forces are frequently below human perceptual thresholds. Further, the interaction between the tool shaft and sclera makes accurate sensing of forces exerted on the retina very difficult with previously developed force sensing schemes, in which the sensor is located outside the eye. In the work reported here, we incorporate 160 µm Fiber Bragg Grating (FBG) strain sensors into the tool shaft to sense forces distal to the sclera. The sensor is applicable both with robotically manipulated and freehand tools. Preliminary results with a 1 degree-of-freedom (DOF) sensor have demonstrated 0.25 mN resolution, and work is underway to develop 2 and 3 DOF tools. The design and analysis of the force sensing tool is presented with preliminary testing data and some initial experiments using the tool with both freehand and robotic manipulation.

Magnetic Resonance Imaging (MRI) has potential to be a superior medical imaging modality for guiding and monitoring prostatic interventions. The strong magnetic field prevents the use of conventional mechatronics and the confined physical space makes it extremely challenging to access the patient. We have designed a robotic assistant system that overcomes these difficulties and promises safe and reliable intra-prostatic needle placement inside closed high-field MRI scanners. The robot performs needle insertion under real-time 3T MR image guidance; workspace requirements, MR compatibility, and workflow have been evaluated on phantoms. The paper explains the robot mechanism and controller design and presents results of preliminary evaluation of the system.

This paper describes the development and initial testing of a new and optimized version of a steady-hand manipulator for retinal microsurgery. In the steady-hand paradigm, the surgeon and the robot share control of a tool attached to the robot through a force sensor. The robot controller senses forces exerted by the operator on the tool and uses this information in various control modes to provide smooth, tremor-free, precise positional control and force scaling. The steady-hand manipulator reported here has been specifically designed with the unique constraints of retinal microsurgery in mind. In particular, the system makes use of a compact wrist design that places the bulk of the robot away from the operating field. The resulting system has high efficacy, flexibility and ergonomics while meeting the accuracy and safety requirements of microsurgery. We have now tested this robot on a biological model system and we report a protocol for reliably cannulating ~80 mum OD veins (the size of veins in the human retina) using the system