Towards Motion Compensation in Autonomous Robotic Subretinal Injections

Exudative (wet) age-related macular degeneration (AMD) is a leading cause of vision loss in older adults, typically treated with intravitreal injections. Emerging therapies, such as subretinal injections of stem cells, gene therapy, small molecules or RPE cells require precise delivery to avoid damaging delicate retinal structures. Autonomous robotic systems can potentially offer the necessary precision for these procedures. This paper presents a novel approach for motion compensation in robotic subretinal injections, utilizing real-time Optical Coherence Tomography (OCT). The proposed method leverages B$^{5}$-scans, a rapid acquisition of small-volume OCT data, for dynamic tracking of retinal motion along the Z-axis, compensating for physiological movements such as breathing and heartbeat. Validation experiments on \textit{ex vivo} porcine eyes revealed challenges in maintaining a consistent tool-to-retina distance, with deviations of up to 200 $\mu m$ for 100 $\mu m$ amplitude motions and over 80 $\mu m$ for 25 $\mu m$ amplitude motions over one minute. Subretinal injections faced additional difficulties, with horizontal shifts causing the needle to move off-target and inject into the vitreous. These results highlight the need for improved motion prediction and horizontal stability to enhance the accuracy and safety of robotic subretinal procedures.

Minimally Invasive Flexible Needle Manipulation Based on Finite Element Simulation and Cross Entropy Method

We present a novel approach for minimally invasive flexible needle manipulations by pairing a real-time finite element simulator with the cross-entropy method. Additionally, we demonstrate how a kinematic-driven bang-bang controller can complement the control framework for better tracking performance. We show how electromagnetic (EM) tracking can be readily incorporated into the framework to provide controller feedback. Tissue phantom experiment with EM tracking shows the average targeting error is $0.16 \pm 0.29mm$.

Real-time Deformation-aware Control for Autonomous Robotic Subretinal Injection under iOCT Guidance

Robotic platforms provide repeatable and precise tool positioning that significantly enhances retinal microsurgery. Integration of such systems with intraoperative optical coherence tomography (iOCT) enables image-guided robotic interventions, allowing to autonomously perform advanced treatment possibilities, such as injecting therapeutic agents into the subretinal space. Yet, tissue deformations due to tool-tissue interactions are a major challenge in autonomous iOCT-guided robotic subretinal injection, impacting correct needle positioning and, thus, the outcome of the procedure. This paper presents a novel method for autonomous subretinal injection under iOCT guidance that considers tissue deformations during the insertion procedure. This 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, to monitor the positioning of the instrument regarding a virtual target layer defined at a relative position 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 previous autonomous insertion approaches. Compared to a 35% success rate in subretinal bleb generation with previous approaches, our proposed method reliably and robustly created subretinal blebs in all our experiments.

Bimanual Manipulation of Steady Hand Eye Robots with Adaptive Sclera Force Control: Cooperative vs. Teleoperation Strategies

Performing intricate eye microsurgery, such as 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 has to do with 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 address crucial safety considerations, such as minimizing contact force between the surgical instrument and the sclera surface to prevent tissue damage. An adaptive sclera force control algorithm was proposed to address this limitation using an FBG-based force-sensing tool to measure and minimize the tool-sclera interaction force. Additionally, features like haptic feedback or hand motion scaling, which can improve the safety and precision of surgery, require a teleoperation control framework. We implemented a bimanual adaptive teleoperation (BMAT) control mode using SHER 2.0 and SHER 2.1 and compared its performance with a bimanual adaptive cooperative (BMAC) mode. Both BMAT and BMAC modes were tested in sitting and standing postures during a vessel-following experiment under a surgical microscope. It is shown, for the first time to the best of our knowledge in robot-assisted retinal surgery, that integrating the adaptive sclera force control algorithm with the bimanual teleoperation framework enables surgeons to safely perform bimanual telemanipulation of the eye without over-stretching it, even in the absence of registration between the two robots.

Shape Manipulation of Bevel-Tip Needles for Prostate Biopsy Procedures: A Comparison of Two Resolved-Rate Controllers

Prostate cancer diagnosis continues to encounter challenges, often due to imprecise needle placement in standard biopsies. Several control strategies have been developed to compensate for needle tip prediction inaccuracies, however none were compared against each other, and it is unclear whether any of them can be safely and universally applied in clinical settings. This paper compares the performance of two resolved-rate controllers, derived from a mechanics-based and a data-driven approach, for bevel-tip needle control using needle shape manipulation through a template. We demonstrate for a simulated 12-core biopsy procedure under model parameter uncertainty that the mechanics-based controller can better reach desired targets when only the final goal configuration is presented even with uncertainty on model parameters estimation, and that providing a feasible needle path is crucial in ensuring safe surgical outcomes when either controller is used for needle shape manipulation.

Cooperative vs. Teleoperation Control of the Steady Hand Eye Robot with Adaptive Sclera Force Control: A Comparative Study

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.

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

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

Steady-Hand Eye Robot 3.0: Optimization and Benchtop Evaluation for Subretinal Injection

Subretinal injection methods and other procedures for treating retinal conditions and diseases (many considered incurable) have been limited in scope due to limited human motor control. This study demonstrates the next generation, cooperatively controlled Steady-Hand Eye Robot (SHER 3.0), a precise and intuitive-to-use robotic platform achieving clinical standards for targeting accuracy and resolution for subretinal injections. The system design and basic kinematics are reported and a deflection model for the incorporated delta stage and validation experiments are presented. This model optimizes the delta stage parameters, maximizing the global conditioning index and minimizing torsional compliance. Five tests measuring accuracy, repeatability, and deflection show the optimized stage design achieves a tip accuracy of <30 $\mu$m, tip repeatability of 9.3 $\mu$m and 0.02{\deg}, and deflections between 20-350 $\mu$m/N. Future work will use updated control models to refine tip positioning outcomes and will be tested on in vivo animal models.

Micromanipulation in Surgery: Autonomous Needle Insertion Inside the Eye for Targeted Drug Delivery

We consider a micromanipulation problem in eye surgery, specifically retinal vein cannulation (RVC). RVC involves inserting a microneedle into a retinal vein for the purpose of targeted drug delivery. The procedure requires accurately guiding a needle to a target vein and inserting it while avoiding damage to the surrounding tissues. RVC can be considered similar to the reach or push task studied in robotics manipulation, but with additional constraints related to precision and safety while interacting with soft tissues. Prior works have mainly focused developing robotic hardware and sensors to enhance the surgeons' accuracy, leaving the automation of RVC largely unexplored. In this paper, we present the first autonomous strategy for RVC while relying on a minimal setup: a robotic arm, a needle, and monocular images. Our system exclusively relies on monocular vision to achieve precise navigation, gentle placement on the target vein, and safe insertion without causing tissue damage. Throughout the procedure, we employ machine learning for perception and to identify key surgical events such as needle-vein contact and vein punctures. Detecting these events guides our task and motion planning framework, which generates safe trajectories using model predictive control to complete the procedure. We validate our system through 24 successful autonomous trials on 4 cadaveric pig eyes. We show that our system can navigate to target veins within 22 micrometers of XY accuracy and under 35 seconds, and consistently puncture the target vein without causing tissue damage. Preliminary comparison to a human demonstrates the superior accuracy and reliability of our system.

Deep Learning Guided Autonomous Surgery: Guiding Small Needles into Sub-Millimeter Scale Blood Vessels

We propose a general strategy for autonomous guidance and insertion of a needle into a retinal blood vessel. The main challenges underpinning this task are the accurate placement of the needle-tip on the target vein and a careful needle insertion maneuver to avoid double-puncturing the vein, while dealing with challenging kinematic constraints and depth-estimation uncertainty. Following how surgeons perform this task purely based on visual feedback, we develop a system which relies solely on \emph{monocular} visual cues by combining data-driven kinematic and contact estimation, visual-servoing, and model-based optimal control. By relying on both known kinematic models, as well as deep-learning based perception modules, the system can localize the surgical needle tip and detect needle-tissue interactions and venipuncture events. The outputs from these perception modules are then combined with a motion planning framework that uses visual-servoing and optimal control to cannulate the target vein, while respecting kinematic constraints that consider the safety of the procedure. We demonstrate that we can reliably and consistently perform needle insertion in the domain of retinal surgery, specifically in performing retinal vein cannulation. Using cadaveric pig eyes, we demonstrate that our system can navigate to target veins within 22$\mu m$ XY accuracy and perform the entire procedure in less than 35 seconds on average, and all 24 trials performed on 4 pig eyes were successful. Preliminary comparison study against a human operator show that our system is consistently more accurate and safer, especially during safety-critical needle-tissue interactions. To the best of the authors' knowledge, this work accomplishes a first demonstration of autonomous retinal vein cannulation at a clinically-relevant setting using animal tissues.