arXiv subjects

Iulian Iordachita

Publications and source records attributed to Iulian Iordachita.

At least 19 recordsLinked to original sources

arXiv2026

An Extended Generalized Prandtl-Ishlinskii Hysteresis Model for I2RIS Robot

Retinal surgery requires extreme precision due to constrained anatomical spaces in the human retina. To assist surgeons achieve this level of accuracy, the Improved Integrated Robotic Intraocular Snake (I2RIS) with dexterous capability has been developed. However, such flexible tendon-driven robots often suffer from hysteresis problems, which significantly challenges precise control and positioning. In particular, we observed multi-stage hysteresis phenomena in the small-scale I2RIS. In this paper, we propose an Extended Generalized Prandtl-Ishlinskii (EGPI) model to increase the fitting accuracy of the hysteresis. The model incorporates a novel switching mechanism that enables it to describe multi-stage hysteresis in the regions of monotonic input. Experimental validation on I2RIS data demonstrates that the EGPI model outperforms the conventional Generalized Prandtl-Ishlinskii (GPI) model in terms of RMSE, NRMSE, and MAE across multiple motor input directions. The EGPI model in our study highlights the potential in modeling multi-stage hysteresis in minimally invasive flexible robots.

cs.RO↗

arXiv2025

An image-guided high-precision research platform for ultra-high dose rate spinal cord toxicity studies

Objective: While FLASH radiotherapy is recognized for short-term normal tissue sparing, its durability in late-responding organs remains uncertain, limiting clinical adoption. With its clinical importance and steep dose-response, the spinal cord is an ideal model for evaluating FLASH effect on late toxicity. This work introduces a robust image-guided research platform for high-precision irradiation at both CONV and UHDR to enable FLASH late toxicity studies using a rat spinal cord model. Approach: A modified LINAC was employed to irradiate the C1-T2 rat spinal cord with 18 MeV UHDR and CONV beams. A custom rat immobilization device, a portable X-ray imaging system, and an ion-chamber-based UHDR output monitoring system were integrated to ensure accurate C1-T2 localization and precise dose delivery. A Monte Carlo (MC) dose engine was developed to provide accurate dosimetry and support interpretation of in vivo results. Scintillator measurements were performed within the spinal cord to verify MC results and the precision of our platform. Results: We achieved submillimeter C1-T2 setup accuracy and maintained submillimeter intrafraction motion. Ion chamber readings showed linear correlation with UHDR output. MC indicated uniform irradiation along the central ~13 mm cord. Our CONV beam exhibited distribution close to that of the UHDR beam, with difference <3%, isolating dose rate as the only variable. Scintillator-measured dose agreed with MC within 4%, confirming both MC accuracy and the platform's high-precision delivery. Significance: We developed the first comprehensive, image-guided preclinical platform for accurate UHDR and CONV irradiation to investigate FLASH-mitigated spinal cord toxicity in rats. This work thus establishes a robust foundation for systematic evaluation of the FLASH effect in late-responding organs and for determining clinical applicability of FLASH.

physics.med-ph↗

arXiv2025

Deep Learning-Enhanced Robotic Subretinal Injection with Real-Time Retinal Motion Compensation

Subretinal injection is a critical procedure for delivering therapeutic agents to treat retinal diseases such as age-related macular degeneration (AMD). However, retinal motion caused by physiological factors such as respiration and heartbeat significantly impacts precise needle positioning, increasing the risk of retinal pigment epithelium (RPE) damage. This paper presents a fully autonomous robotic subretinal injection system that integrates intraoperative optical coherence tomography (iOCT) imaging and deep learning-based motion prediction to synchronize needle motion with retinal displacement. A Long Short-Term Memory (LSTM) neural network is used to predict internal limiting membrane (ILM) motion, outperforming a Fast Fourier Transform (FFT)-based baseline model. Additionally, a real-time registration framework aligns the needle tip position with the robot's coordinate frame. Then, a dynamic proportional speed control strategy ensures smooth and adaptive needle insertion. Experimental validation in both simulation and ex vivo open-sky porcine eyes demonstrates precise motion synchronization and successful subretinal injections. The experiment achieves a mean tracking error below 16.4 μm in pre-insertion phases. These results show the potential of AI-driven robotic assistance to improve the safety and accuracy of retinal microsurgery.

cs.RO↗

arXiv2025

A universal bioluminescence tomography system for pre-clinical image-guided radiotherapy research

CBCT-guided small animal irradiators encounter challenges in localizing soft-tissue targets due to low imaging contrast. Bioluminescence tomography (BLT) offers a promising solution, but they have largely remained in laboratorial development, limiting accessibility for researchers. In this work, we develop a universal, commercial-graded BLT-guided system (MuriGlo) designed to seamlessly integrate with commercial irradiators and empower researchers for translational studies. We demonstrate its capabilities in supporting in vitro and in vivo studies. The MuriGlo comprises detachable mouse bed, thermostatic control, mirrors, filters, and CCD, enabling multi-projection and multi-spectral imaging. We evaluate that the thermostatic control effectively sustains animal temperature at 37°C throughout imaging, and quantify that the system can detect as few as 61 GL261-AkaLuc cells in vitro. To illustrate how the MuriGlo can be utilized for in vivo image-guided research, we present 3 strategies, BLT-guided 5-arc, 2-field box, and BLI-guided single-beam, ranging from complicated high-conformal to simplest high-throughput plans. The high conformal BLT-guided 5-arc plan fully covers the gross tumor volume (GTV) at prescribed dose with minimal normal tissue exposure (3.9%), while the simplified, high-throughput BLT-guided 2-field box achieves 100% GTV coverage but results in higher normal tissue exposure (13.1%). Moreover, we demonstrate that the localization accuracy of MuriGlo for both widely-used SARRP and SmART irradiators is within1 mm, and the tumor coverage reaches over 97% with 0.75mm margin. The universal BLT-guided system offers seamless integration with commercial irradiators, achieving comparable localization accuracy, expected to supporting high-precision radiation research.

physics.med-ph↗

arXiv2025

Model Predictive Path Integral Control of I2RIS Robot Using RBF Identifier and Extended Kalman Filter

Modeling and controlling cable-driven snake robots is a challenging problem due to nonlinear mechanical properties such as hysteresis, variable stiffness, and unknown friction between the actuation cables and the robot body. This challenge is more significant for snake robots in ophthalmic surgery applications, such as the Improved Integrated Robotic Intraocular Snake (I$^2$RIS), given its small size and lack of embedded sensory feedback. Data-driven models take advantage of global function approximations, reducing complicated analytical models' challenge and computational costs. However, their performance might deteriorate in case of new data unseen in the training phase. Therefore, adding an adaptation mechanism might improve these models' performance during snake robots' interactions with unknown environments. In this work, we applied a model predictive path integral (MPPI) controller on a data-driven model of the I$^2$RIS based on the Gaussian mixture model (GMM) and Gaussian mixture regression (GMR). To analyze the performance of the MPPI in unseen robot-tissue interaction situations, unknown external disturbances and environmental loads are simulated and added to the GMM-GMR model. These uncertainties of the robot model are then identified online using a radial basis function (RBF) whose weights are updated using an extended Kalman filter (EKF). Simulation results demonstrated the robustness of the optimal control solutions of the MPPI algorithm and its computational superiority over a conventional model predictive control (MPC) algorithm.

cs.RO↗

arXiv2025

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

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 B${^5}$-scans. Using B${^5}$-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.

cs.RO↗

arXiv2025

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 and RPE cells require precise delivery to avoid damaging delicate retinal structures. 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 ex vivo porcine eyes revealed challenges in maintaining a consistent tool-to-retina distance, with deviations of up to 200 $μm$ for 100 $μm$ amplitude motions and over 80 $μm$ for 25 $μm$ amplitude motions over one minute. Subretinal injections faced additional difficulties, with phase 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.

cs.RO↗

arXiv2024

A Feasible Workflow for Retinal Vein Cannulation in Ex Vivo Porcine Eyes with Robotic Assistance

A potential Retinal Vein Occlusion (RVO) treatment involves Retinal Vein Cannulation (RVC), which requires the surgeon to insert a microneedle into the affected retinal vein and administer a clot-dissolving drug. This procedure presents significant challenges due to human physiological limitations, such as hand tremors, prolonged tool-holding periods, and constraints in depth perception using a microscope. This study proposes a robot-assisted workflow for RVC to overcome these limitations. The test robot is operated through a keyboard. An intraoperative Optical Coherence Tomography (iOCT) system is used to verify successful venous puncture before infusion. The workflow is validated using 12 ex vivo porcine eyes. These early results demonstrate a successful rate of 10 out of 12 cannulations (83.33%), affirming the feasibility of the proposed workflow.

cs.RO↗

arXiv2024

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 $0.16 \pm 0.29mm$.

cs.RO↗

arXiv2024

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

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. Additionally, features like haptic feedback or hand motion scaling, which can improve the safety and precision of surgery, require a teleoperation control framework. This work presents a bimanual adaptive teleoperation (BMAT) control framework using SHER 2.0 and SHER 2.1 robotic systems. We integrate them with an adaptive force control (AFC) algorithm to automatically minimize the tool-sclera interaction force. The scleral forces are measured using two fiber Bragg grating (FBG)-based force-sensing tools. We compare the performance of the BMAT mode with a bimanual adaptive cooperative (BMAC) mode in a vessel-following experiment under a surgical microscope. Experimental results demonstrate the effectiveness of the proposed 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.

cs.RO↗

arXiv2024

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.

cs.RO↗

arXiv2023

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.

cs.RO↗

arXiv2023

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.

cs.RO↗

arXiv2023

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 $μ$m, tip repeatability of 9.3 $μ$m and 0.02°, and deflections between 20-350 $μ$m/N. Future work will use updated control models to refine tip positioning outcomes and will be tested on in vivo animal models.

cs.RO↗

arXiv2023

Towards Deep Learning Guided Autonomous Eye Surgery Using Microscope and iOCT Images

Recent advancements in retinal surgery have paved the way for a modern operating room equipped with a surgical robot, a microscope, and intraoperative optical coherence tomography (iOCT)- a depth sensor widely used in retinal surgery. Integrating these tools raises the fundamental question of how to effectively combine them to enable surgical autonomy. In this work, we tackle this question by developing a unified framework that facilitates real-time autonomous surgical workflows leveraging these devices. The system features: (1) a novel imaging system that integrates the microscope and iOCT in real-time by dynamically tracking the surgical instrument via a small iOCT scanning region, providing real-time depth feedback; (2) implementation of convolutional neural networks (CNN) that automatically detect and segment task-relevant information for surgical autonomy; (3) intuitive selection of goal waypoints within both the microscope and iOCT views through simple mouse-click interactions; and (4) integration of model predictive control (MPC) for trajectory generation, ensuring patient safety by implementing safety-related kinematic constraints. The system's utility is demonstrated by automating subretinal injection (SI), a challenging procedure with high accuracy and depth perception requirements. We validate our system by conducting 30 successful SI trials on pig eyes, achieving mean needle insertion accuracy of 26 micrometers to various subretinal goals and mean duration of 55 seconds. Preliminary comparisons to a human operator performing SI in robot-assisted mode highlight the enhanced safety of our system. Project website is here: https://sites.google.com/view/eyesurgerymicroscopeoct/home

cs.RO↗

arXiv2023

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.

cs.RO↗

arXiv2023

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$μm$ 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.

cs.RO↗

arXiv2023

Human-Robot Interaction in Retinal Surgery: A Comparative Study of Serial and Parallel Cooperative Robots

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.

cs.RO↗