Hybrid Deep Learning and Model-Based Needle Shape Prediction.

Needle insertion using flexible bevel tip needles are a common minimally-invasive surgical technique for prostate cancer interventions. Flexible, asymmetric bevel tip needles enable physicians for complex needle steering techniques to avoid sensitive anatomical structures during needle insertion. For accurate placement of the needle, predicting the trajectory of these needles intra-operatively would greatly reduce the need for frequently needle reinsertions thus improving patient comfort and positive outcomes. However, predicting the trajectory of the needle during insertion is a complex task that has yet to be solved due to random needle-tissue interactions. In this paper, we present and validate for the first time a hybrid deep learning and model-based approach to handle the intra-operative needle shape prediction problem through, leveraging a validated Lie-group theoretic model for needle shape representation. Furthermore, we present a novel self-supervised learning and method in conjunction with the Lie-group shape model for training these networks in the absence of data, enabling further refinement of these networks with transfer learning. Needle shape prediction was performed in single-layer and double-layer homogeneous phantom tissue for C- and S-shape needle insertions. Our method demonstrates an average root-mean-square prediction error of 1.03 mm over a dataset containing approximately 3,000 prediction samples with maximum prediction steps of 110 mm.

Design and Fabrication of a Fiber Bragg Grating Shape Sensor for Shape Reconstruction of a Continuum Manipulator.

Continuum dexterous manipulators (CDMs) are suitable for performing tasks in a constrained environment due to their high dexterity and maneuverability. Despite the inherent advantages of CDMs in minimally invasive surgery, real-time control of CDMs' shape during nonconstant curvature bending is still challenging. This study presents a novel approach for the design and fabrication of a large deflection fiber Bragg grating (FBG) shape sensor embedded within the lumens inside the walls of a CDM with a large instrument channel. The shape sensor consisted of two fibers, each with three FBG nodes. A shape-sensing model was introduced to reconstruct the centerline of the CDM based on FBG wavelengths. Different experiments, including shape sensor tests and CDM shape reconstruction tests, were conducted to assess the overall accuracy of the shape-sensing. The FBG sensor evaluation results revealed the linear curvature-wavelength relationship with the large curvature detection of 0.045 mm and a high wavelength shift of up to 5.50 nm at a 90° bending angle in both the bending directions. The CDM's shape reconstruction experiments in a free environment demonstrated the shape-tracking accuracy of 0.216 ± 0.126 mm for positive/negative deflections. Also, the CDM shape reconstruction error for three cases of bending with obstacles was observed to be 0.436 ± 0.370 mm for the proximal case, 0.485 ± 0.418 mm for the middle case, and 0.312 ± 0.261 mm for the distal case. This study indicates the adequate performance of the FBG sensor and the effectiveness of the model for tracking the shape of the large-deflection CDM with nonconstant-curvature bending for minimally invasive orthopedic applications.

Simultaneous Online Registration-Independent Stiffness Identification and Tip Localization of Surgical Instruments in Robot-assisted Eye Surgery.

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.

Robotic Assistance for Intraocular Microsurgery: Challenges and Perspectives.

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.

State of the Art and Future Opportunities in MRI-Guided Robot-Assisted Surgery and Interventions.

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.

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

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

A Dexterous Robotic System for Autonomous Debridement of Osteolytic Bone Lesions in Confined Spaces: Human Cadaver Studies.

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.

MRI-guided lumbar spinal injections with body-mounted robotic system: cadaver studies.

INTRODUCTION: This paper reports the system integration and cadaveric assessment of a body-mounted robotic system for MRI-guided lumbar spine injections. The system is developed to enable MR-guided interventions in closed bore magnet and avoid problems due to patient movement during cannula guidance. MATERIAL AND METHODS: The robot is comprised by a lightweight and compact structure so that it can be mounted directly onto the lower back of a patient using straps. Therefore, it can minimize the influence of patient movement by moving with the patient. The MR-Conditional robot is integrated with an image-guided surgical planning workstation. A dedicated clinical workflow is created for the robot-assisted procedure to improve the conventional freehand MRI-guided procedure. RESULTS: Cadaver studies were performed with both freehand and robot-assisted approaches to validate the feasibility of the clinical workflow and to assess the positioning accuracy of the robotic system. The experiment results demonstrate that the root mean square (RMS) error of the target position to be 2.57 ± 1.09 mm and of the insertion angle to be 2.17 ± 0.89°. CONCLUSION: The robot-assisted approach is able to provide more accurate and reproducible cannula placements than the freehand procedure, as well as to reduce the number of insertion attempts.

Data-Driven Shape Sensing of a Surgical Continuum Manipulator Using an Uncalibrated Fiber Bragg Grating Sensor.

This article proposes a data-driven learning-based approach for shape sensing and Distal-end Position Estimation (DPE) of a surgical Continuum Manipulator (CM) in constrained environments using Fiber Bragg Grating (FBG) sensors. The proposed approach uses only the sensory data from an unmodeled uncalibrated sensor embedded in the CM to estimate the shape and DPE. It serves as an alternate to the conventional mechanics-based sensor-model-dependent approach which relies on several sensor and CM geometrical assumptions. Unlike the conventional approach where the shape is reconstructed from proximal to distal end of the device, we propose a reversed approach where the distal-end position is estimated first and given this information, shape is then reconstructed from distal to proximal end. The proposed methodology yields more accurate DPE by avoiding accumulation of integration errors in conventional approaches. We study three data-driven models, namely a linear regression model, a Deep Neural Network (DNN), and a Temporal Neural Network (TNN) and compare DPE and shape reconstruction results. Additionally, we test both approaches (data-driven and model-dependent) against internal and external disturbances to the CM and its environment such as incorporation of flexible medical instruments into the CM and contacts with obstacles in taskspace. Using the data-driven (DNN) and model-dependent approaches, the following max absolute errors are observed for DPE: 0.78 mm and 2.45 mm in free bending motion, 0.11 mm and 3.20 mm with flexible instruments, and 1.22 mm and 3.19 mm with taskspace obstacles, indicating superior performance of the proposed data-driven approach compared to the conventional approaches.

Stochastic Force-based Insertion Depth and Tip Position Estimations of Flexible FBG-Equipped Instruments in Robotic Retinal Surgery.

Vitreoretinal surgery is among the most delicate surgical tasks during which surgeon hand tremor may severely attenuate surgeon performance. Robotic assistance has been demonstrated to be beneficial in diminishing hand tremor. Among the requirements for reliable assistance from the robot is to provide precise measurements of system states e.g. sclera forces, tool tip position and tool insertion depth. Providing this and other sensing information using existing technology would contribute towards development and implementation of autonomous robot-assisted tasks in retinal surgery such as laser ablation, guided suture placement/assisted needle vessel cannulation, among other applications. In the present work, we use a state-estimating Kalman filtering (KF) to improve the tool tip position and insertion depth estimates, which used to be purely obtained by robot forward kinematics (FWK) and direct sensor measurements, respectively. To improve tool tip localization, in addition to robot FWK, we also use sclera force measurements along with beam theory to account for tool deflection. For insertion depth, the robot FWK is combined with sensor measurements for the cases where sensor measurements are not reliable enough. The improved tool tip position and insertion depth measurements are validated using a stereo camera system through preliminary experiments and a case study. The results indicate that the tool tip position and insertion depth measurements are significantly improved by 77% and 94% after applying KF, respectively.

A Surgical Robotic System for Treatment of Pelvic Osteolysis Using an FBG-Equipped Continuum Manipulator and Flexible Instruments.

This paper presents the development and experimental evaluation of a redundant robotic system for the less-invasive treatment of osteolysis (bone degradation) behind the acetabular implant during total hip replacement revision surgery. The system comprises a rigid-link positioning robot and a Continuum Dexterous Manipulator (CDM) equipped with highly flexible debriding tools and a Fiber Bragg Grating (FBG)-based sensor. The robot and the continuum manipulator are controlled concurrently via an optimization-based framework using the Tip Position Estimation (TPE) from the FBG sensor as feedback. Performance of the system is evaluated on a setup that consists of an acetabular cup and saw-bone phantom simulating the bone behind the cup. Experiments consist of performing the surgical procedure on the simulated phantom setup. CDM TPE using FBGs, target location placement, cutting performance, and the concurrent control algorithm capability in achieving the desired tasks are evaluated. Mean and standard deviation of the CDM TPE from the FBG sensor and the robotic system are 0.50 mm, and 0.18 mm, respectively. Using the developed surgical system, accurate positioning and successful cutting of desired straight-line and curvilinear paths on saw-bone phantoms behind the cup with different densities are demonstrated. Compared to the conventional rigid tools, the workspace reach behind the acetabular cup is 2.47 times greater when using the developed robotic system.

A Mosquito Pick-and-Place System for PfSPZ-based Malaria Vaccine Production.

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. NOTE TO PRACTITIONERS­: Automated processes could help increase malaria vaccine production to global scale. Currently, production requires technicians to manually dissect mosquitoes, a process that is slow, tedious, and requires a lengthy training regimen. This paper presents an an improved manual fixture and procedure that reduces technician training time. Further, an approach to automate this dissection process is proposed and the critical step of robotic manipulation of the mosquito with the aid of computer vision is demonstrated. Our approach may serve as a useful example of system design and integration for practitioners that seek to perform new and challenging pick-and-place tasks with small, non-uniform, and highly deformable objects.

SCADE: Simultaneous Sensor Calibration and Deformation Estimation of FBG-Equipped Unmodeled Continuum Manipulators.

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.

Automatic Light Pipe Actuating System for Bimanual Robot-Assisted Retinal Surgery.

Retinal surgery is a bimanual operation in which surgeons operate with an instrument in their dominant hand (more capable hand) and simultaneously hold a light pipe (illuminating pipe) with their non-dominant hand (less capable hand) to provide illumination inside the eye. Manually holding and adjusting the light pipe places an additional burden on the surgeon and increases the overall complexity of the procedure. To overcome these challenges, a robot-assisted automatic light pipe actuating system is proposed. A customized light pipe with force-sensing capability is mounted at the end effector of a follower robot and is actuated through a hybrid force-velocity controller to automatically illuminate the target area on the retinal surface by pivoting about the scleral port (incision on the sclera). Static following-accuracy evaluation and dynamic light tracking experiments are carried out. The results show that the proposed system can successfully illuminate the desired area with negligible offset (the average offset is 2.45 mm with standard deviation of 1.33 mm). The average scleral forces are also below a specified threshold (50 mN). The proposed system not only can allow for increased focus on dominant hand instrument control, but also could be extended to three-arm procedures (two surgical instruments held by surgeon plus a robot-holding light pipe) in retinal surgery, potentially improving surgical efficiency and outcome.

Distributed fiber optics 3D shape sensing by means of high scattering NP-doped fibers simultaneous spatial multiplexing.

A novel approach for fiber optics 3D shape sensing, applicable to mini-invasive bio-medical devices, is presented. The approach exploits the optical backscatter reflectometry (OBR) and an innovative setup that permits the simultaneous spatial multiplexing of an optical fibers parallel. The result is achieved by means of a custom-made enhanced backscattering fiber whose core is doped with MgO-based nanoparticles (NP). This special NP-doped fiber presents a backscattering-level more than 40 dB higher with respect to a standard SMF-28. The fibers parallel is built to avoid overlap between NP-doped fibers belonging to different branches of the parallel, so that the OBR can distinguish the more intense backscattered signal coming from the NP-doped fiber. The system is tested by fixing, with epoxy glue, 4 NP-doped fibers along the length of an epidural needle. Each couple of opposite fibers senses the strain on a perpendicular direction. The needle is inserted in a custom-made phantom that simulates the spine anatomy. The 3D shape sensing is obtained by converting the measured strain in bending and shape deformation.

Robotic Vitreoretinal Surgery.

PURPOSE: To review the current literature on robotic assistance for ophthalmic surgery, especially vitreoretinal procedures. METHODS: MEDLINE, Embase, and Web of Science databases were searched from inception to August, 2016, for articles relevant to the review topic. Queries included combinations of the terms: robotic eye surgery, ophthalmology, and vitreoretinal. RESULTS: In ophthalmology, proof-of-concept papers have shown the feasibility of performing many delicate anterior segment and vitreoretinal surgical procedures accurately with robotic assistance. Multiple surgical platforms have been designed and tested in animal eyes and phantom models. These platforms have the capability to measure forces generated and velocities of different surgical movements. "Smart" instruments have been designed to improve certain tasks such as membrane peeling and retinal vessel cannulations. CONCLUSION: Ophthalmic surgery, particularly vitreoretinal surgery, might have reached the limits of human physiologic performance. Robotic assistance can help overcome biologic limitations and improve our surgical performance. Clinical studies of robotic-assisted surgeries are needed to determine safety and feasibility of using this technology in patients.

3-DOF Force-Sensing Motorized Micro-Forceps for Robot-Assisted Vitreoretinal Surgery.

In vitreoretinal surgery, membrane peeling is a prototypical task where a layer of fibrous tissue is delaminated off the retina with a micro-forceps by applying very fine forces that are mostly imperceptible to the surgeon. Previously we developed sensitized ophthalmic surgery tools based on fiber Bragg grating (FBG) strain sensors, which were shown to precisely detect forces at the instrument's tip in two degrees of freedom perpendicular to the tool axis. This paper presents a new design that employs an additional sensor to capture also the tensile force along the tool axis. The grasping functionality is provided via a compact motorized unit. To compute forces, we investigate two distinct fitting methods: a linear regression and a nonlinear fitting based on second-order Bernstein polynomials. We carry out experiments to test the repeatability of sensor outputs, calibrate the sensor and validate its performance. Results demonstrate sensor wavelength repeatability within 2 pm. Although the linear method provides sufficient accuracy in measuring transverse forces, in the axial direction it produces a root mean square (rms) error over 3 mN even for a confined magnitude and direction of forces. On the other hand, the nonlinear method provides a more consistent and accurate measurement of both the transverse and axial forces for the entire force range (0-25 mN). Validation including random samples shows that our tool with the nonlinear force computation method can predict 3-D forces with an rms error under 0.15 mN in the transverse plane and within 2 mN accuracy in the axial direction.

Fiber Optic Force Sensors for MRI-Guided Interventions and Rehabilitation: A Review.

Magnetic Resonance Imaging (MRI) provides both anatomical imaging with excellent soft tissue contrast and functional MRI imaging (fMRI) of physiological parameters. The last two decades have witnessed the manifestation of increased interest in MRI-guided minimally invasive intervention procedures and fMRI for rehabilitation and neuroscience research. Accompanying the aspiration to utilize MRI to provide imaging feedback during interventions and brain activity for neuroscience study, there is an accumulated effort to utilize force sensors compatible with the MRI environment to meet the growing demand of these procedures, with the goal of enhanced interventional safety and accuracy, improved efficacy and rehabilitation outcome. This paper summarizes the fundamental principles, the state of the art development and challenges of fiber optic force sensors for MRI-guided interventions and rehabilitation. It provides an overview of MRI-compatible fiber optic force sensors based on different sensing principles, including light intensity modulation, wavelength modulation, and phase modulation. Extensive design prototypes are reviewed to illustrate the detailed implementation of these principles. Advantages and disadvantages of the sensor designs are compared and analyzed. A perspective on the future development of fiber optic sensors is also presented which may have additional broad clinical applications. Future surgical interventions or rehabilitation will rely on intelligent force sensors to provide situational awareness to augment or complement human perception in these procedures.

Feasibility study of ultrasound imaging for stereotactic body radiation therapy with active breathing coordinator in pancreatic cancer.

PURPOSE: Stereotactic body radiation therapy (SBRT) allows for high radiation doses to be delivered to the pancreatic tumors with limited toxicity. Nevertheless, the respiratory motion of the pancreas introduces major uncertainty during SBRT. Ultrasound imaging is a non-ionizing, non-invasive, and real-time technique for intrafraction monitoring. A configuration is not available to place the ultrasound probe during pancreas SBRT for monitoring. METHODS AND MATERIALS: An arm-bridge system was designed and built. A CT scan of the bridge-held ultrasound probe was acquired and fused to ten previously treated pancreatic SBRT patient CTs as virtual simulation CTs. Both step-and-shoot intensity-modulated radiation therapy (IMRT) and volumetric-modulated arc therapy (VMAT) planning were performed on virtual simulation CT. The accuracy of our tracking algorithm was evaluated by programmed motion phantom with simulated breath-hold 3D movement. An IRB-approved volunteer study was also performed to evaluate feasibility of system setup. Three healthy subjects underwent the same patient setup required for pancreas SBRT with active breath control (ABC). 4D ultrasound images were acquired for monitoring. Ten breath-hold cycles were monitored for both phantom and volunteers. For the phantom study, the target motion tracked by ultrasound was compared with motion tracked by the infrared camera. For the volunteer study, the reproducibility of ABC breath-hold was assessed. RESULTS: The volunteer study results showed that the arm-bridge system allows placement of an ultrasound probe. The ultrasound monitoring showed less than 2 mm reproducibility of ABC breath-hold in healthy volunteers. The phantom monitoring accuracy is 0.14 ± 0.08 mm, 0.04 ± 0.1 mm, and 0.25 ± 0.09 mm in three directions. On dosimetry part, 100% of virtual simulation plans passed protocol criteria. CONCLUSIONS: Our ultrasound system can be potentially used for real-time monitoring during pancreas SBRT without compromising planning quality. The phantom study showed high monitoring accuracy of the system, and the volunteer study showed feasibility of the clinical workflow.

Towards Robot-Assisted Retinal Vein Cannulation: A Motorized Force-Sensing Microneedle Integrated with a Handheld Micromanipulator †.

Retinal vein cannulation is a technically demanding surgical procedure where therapeutic agents are injected into the retinal veins to treat occlusions. The clinical feasibility of this approach has been largely limited by the technical challenges associated with performing the procedure. Among the challenges to successful vein cannulation are identifying the moment of venous puncture, achieving cannulation of the micro-vessel, and maintaining cannulation throughout drug delivery. Recent advances in medical robotics and sensing of tool-tissue interaction forces have the potential to address each of these challenges as well as to prevent tissue trauma, minimize complications, diminish surgeon effort, and ultimately promote successful retinal vein cannulation. In this paper, we develop an assistive system combining a handheld micromanipulator, called "Micron", with a force-sensing microneedle. Using this system, we examine two distinct methods of precisely detecting the instant of venous puncture. This is based on measured tool-tissue interaction forces and also the tracked position of the needle tip. In addition to the existing tremor canceling function of Micron, a new control method is implemented to actively compensate unintended movements of the operator, and to keep the cannulation device securely inside the vein following cannulation. To demonstrate the capabilities and performance of our uniquely upgraded system, we present a multi-user artificial phantom study with subjects from three different surgical skill levels. Results show that our puncture detection algorithm, when combined with the active positive holding feature enables sustained cannulation which is most evident in smaller veins. Notable is that the active holding function significantly attenuates tool motion in the vein, thereby reduces the trauma during cannulation.