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arXiv 2025年

作者： Esfandiari, Mojtaba Du, Pengyuan Wei, Haochen Gehlbach, Peter Munawar, Adnan Kazanzides, Peter Iordachita, Iulian Department of Mechanical Engineering Laboratory for Computational Sensing and Robotics Johns Hopkins University BaltimoreMD21218 United States Department of Computer Science Laboratory for Computational Sensing and Robotics Johns Hopkins University BaltimoreMD21218 United States Laboratory for Computational Sensing and Robotics Johns Hopkins University BaltimoreMD21218 United States Wilmer Eye Institute Johns Hopkins Hospital BaltimoreMD21287 United States

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 (I2RIS), 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 I2RIS 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. © 2025, CC BY.

关键词： Extended Kalman filters

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The Geometry of Optimal Gait Families for Steering Kinematic Locomoting Systems

arXiv

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arXiv 2025年

作者： Choi, Jinwoo Deng, Siming Justus, Nathan Cowan, Noah J. Hatton, Ross L. Institute Oregon State University CorvallisOR United States Laboratory for Computational Sensing and Robotics Department of Mechanical Engineering Johns Hopkins University BaltimoreMD21218 United States

Motion planning for locomotion systems typically requires translating high-level rigid-body tasks into low-level joint trajectories—a process that is straightforward for car-like robots with fixed, unbounded actuation inputs but more challenging for systems like snake robots, where the mapping depends on the current configuration and is constrained by joint limits. In this paper, we focus on generating continuous families of optimal gaits—collections of gaits parameterized by step size or steering rate—to enhance controllability and maneuverability. We uncover the underlying geometric structure of these optimal gait families and propose methods for constructing them using both global and local search strategies, where the local method and the global method compensate each other. The global search approach is robust to nonsmooth behavior, albeit yielding reduced-order solutions, while the local search provides higher accuracy but can be unstable near nonsmooth regions. To demonstrate our framework, we generate optimal gait families for viscous and perfect-fluid three-link swimmers. This work lays a foundation for integrating low-level joint controllers with higher-level motion planners in complex locomotion systems. Copyright © 2025, The Authors. All rights reserved.

关键词： Motion planning

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Gravity Compensation of the dVRK-Si Patient Side Manipulator based on Dynamic Model Identification

arXiv

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arXiv 2025年

作者： Zhou, Haoying Yang, Hao Deguet, Anton Fichera, Loris Wu, Jie Ying Kazanzides, Peter Department of Robotics Engineering Worcester Polytechnic Institute MA United States Laboratory for Computational Sensing and Robotics JHU MD United States Department of Computer Science Vanderbilt University TN United States Department of Computer Science Johns Hopkins University MD United States

The da Vinci Research Kit (dVRK, also known as dVRK Classic) is anopen-source teleoperated surgical robotic system whose hardware is obtainedfrom the first generation da Vinci Surgical System (Intuitive, Sunnyvale, CA,USA). The dVRK has greatly facilitated research in robot-assisted surgery overthe past decade and helped researchers address multiple major challenges inthis domain. Recently, the dVRK-Si system, a new version of the dVRK which usesmechanical components from the da Vinci Si Surgical System, became available tothe community. The major difference between the first generation da Vinci andthe da Vinci Si is in the structural upgrade of the Patient Side Manipulator(PSM). Because of this upgrade, the gravity of the dVRK-Si PSM can no longer beignored as in the dVRK Classic. The high gravity offset may lead to relativelylow control accuracy and longer response time. In addition, althoughsubstantial progress has been made in addressing the dynamic modelidentification problem for the dVRK Classic, further research is required onmodel-based control for the dVRK-Si, due to differences in mechanicalcomponents and the demand for enhanced control performance. To address theseproblems, in this work, we present (1) a novel full kinematic model of thedVRK-Si PSM, and (2) a gravity compensation approach based on the dynamic modelidentification. © 2025, CC BY.

关键词： Surgical equipment

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Modular ultrasound for flexible longitudinal patient monitoring: Feasibility study

Modular ultrasound for flexible longitudinal patient monitor...

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Medical Imaging 2025: Ultrasonic Imaging and Tomography

作者： Tandri, Ananya Boctor, Emad M. Kang, Jeeun Department of Biomedical Engineering Johns Hopkins University School of Medicine BaltimoreMD United States Laboratory for Computational Sensing and Robotics Johns Hopkins University School of Medicine BaltimoreMD United States Department of Anesthesiology and Critical Care Medicine Johns Hopkins University School of Medicine BaltimoreMD United States

ISBN: (纸本)9781510686021

When it comes to longitudinal patient monitoring, traditional medical ultrasound imaging poses several limitations, namely the inevitable manual scanning and restrictions of spatial resolution due to its fixed hand-held probe form factor. Recently, electronic advances have enabled flexible array fabrication, but its clinical efficacy is limited to the predetermined aperture size. Herein, we propose a position-tracked modular ultrasound (MODUS) framework, allowing clinicians to have an ultrasound array in arbitrary aperture size and on flexible surface curvature, which can maintain utility regardless of patient morphology. We first studied how modular configuration affects imaging quality and artifacts in acoustic simulation. A homogenous adult abdominal tissue-mimicking medium was created with point targets placed at abdominal depths up to 24 cm. An arbitrary MODUS array with different configuration for various uniformity, centeredness, and density was simulated on a semi-ellipse shape. Image similarity and mainlobe to grating lobe ratio were evaluated compared to the ground-truth image given by the full aperture. User-dependent error during modular placement was modelled as a gaussian profile, and the comparative evaluation was repeated. Study findings showed that increasing the distance between modules or the irregularity in module placement was associated with a decrease in image similarity metrics, suggesting an impact of grating lobe artifacts. Increased overall aperture width was correlated with improved spatial resolution at increased depths, suggesting better spatial/angular resolution for beamforming. Interestingly, adding modular positional error did not impact image similarity metrics due to statistical maintenance of spatial sampling rate. We also conducted a proof-of-concept study regarding image reconstruction from optical tracking data of single element transducers. Single element 10MHz ultrasound modules attached to 4-point reflective ball markers we

关键词： Neonatal monitoring

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Mask-enhanced Deep-learning for Prostate Ultrasound Tomography with Narrow Data Acquisition Aperture

Mask-enhanced Deep-learning for Prostate Ultrasound Tomograp...

引用

Medical Imaging 2025: Ultrasonic Imaging and Tomography

作者： Wang, Hanchen Wu, Yixuan Boctor, Emad Luo, Songting Lin, Youzuo School of Data Science and Society University of North Carolina at Chapel Hill Chapel HillNC27599 United States Theoretical Division Los Alamos National Laboratory Los AlamosNM87545 United States Laboratory for Computational Sensing and Robotics John Hopkins University BaltimoreMD21218 United States Department of Mathematics College of Liberal Arts and Sciences Iowa State University AmesIA50011 United States

ISBN: (纸本)9781510686021

Prostate cancer is among the most prevalent cancers in men and a leading cause of cancer-related deaths globally. Early detection is critical for effective treatment, yet conventional prostate ultrasound techniques suffer from limited sensitivity and specificity. Ultrasound tomography (UST) offers a potential solution by quantifying parameters such as speed of sound (SOS), but achieving high image quality is challenging due to the narrow data acquisition aperture imposed by the prostate's anatomy. This study explores the use of a convolutional neural network (CNN) to enhance prostate imaging under these constraints. Our approach leverages supervised learning to manage the extremely narrow data acquisition apertures that hinder traditional full-waveform inversion (FWI) methods. We validated our method using synthetic prostate phantoms and finite difference ultrasound data simulations. The results demonstrate that our CNN-based approach surpasses traditional FWI in both accuracy and efficiency, reconstructing high-resolution SOS maps swiftly once the model is trained. This advancement promises significant improvements in the accuracy and efficiency of prostate cancer diagnosis and treatment planning, potentially reducing the need for invasive procedures and enabling better-informed clinical decisions. © 2025 SPIE.

关键词： Ultrasonography

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Towards Fluorescence-Guided Autonomous Robotic Partial Nephrectomy on Novel Tissue-Mimicking Hydrogel Phantoms

arXiv

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arXiv 2025年

作者： Kilmer, Ethan Chen, Joseph Ge, Jiawei Sarda, Preksha Cha, Richard Cleary, Kevin Shepard, Lauren Ghazi, Ahmed Ezzat Scheikl, Paul Maria Krieger, Axel Laboratory of Computational Sensing and Robotics Johns Hopkins University BaltimoreMD21218 United States Department of Computer Science and Software Engineering Rose-Hulman Institute of Technology Terre HauteIN47803 United States Sheikh Zayed Institute for Pediatric Surgical Innovation Children’s National Hospital WashingtonDC20010 United States Department of Urology Johns Hopkins University BaltimoreMD21218 United States

Autonomous robotic systems hold potential for improving renal tumor resection accuracy and patient outcomes. We present a fluorescence-guided robotic system capable of planning and executing incision paths around exophytic renal tumors with a clinically relevant resection margin. Leveraging point cloud observations, the system handles irregular tumor shapes and distinguishes healthy from tumorous tissue based on near-infrared imaging, akin to indocyanine green staining in partial nephrectomy. Tissue-mimicking phantoms are crucial for the development of autonomous robotic surgical systems for interventions where acquiring ex-vivo animal tissue is infeasible, such as cancer of the kidney and renal pelvis. To this end, we propose novel hydrogel-based kidney phantoms with exophytic tumors that mimic the physical and visual behavior of tissue, and are compatible with electrosurgical instruments, a common limitation of silicone-based phantoms. In contrast to previous hydrogel phantoms, we mix the material with near-infrared dye to enable fluorescence-guided tumor segmentation. Autonomous real-world robotic experiments validate our system and phantoms, achieving an average margin accuracy of 1.44 mm in a completion time of 69 sec. Copyright © 2025, The Authors. All rights reserved.

关键词： Phantoms

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

arXiv

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arXiv 2025年

作者： Wu, Tianle Esfandiari, Mojtaba Zhang, Peiyao Taylor, Russell H. Gehlbach, Peter Iordachita, Iulian Laboratory for Computational Sensing and Robotics Johns Hopkins University BaltimoreMD United States Wilmer Eye Institute Johns Hopkins Hospital BaltimoreMD United States

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. © 2025, CC BY.

关键词： Optical coherence tomography

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PAROS: Multi-Component Robotic System and an Image-Guided Patient Alignment for Robot-Assisted Ophthalmic Surgery

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IEEE Access 2025年 13卷 85056-85071页

作者： Alikhani, Alireza Nguyen, Van Dai Inagaki, Satoshi Busam, Benjamin Faridpooya, Koorosh Maier, Mathias Gehlbach, Peter Iordachita, Iulian Navab, Nassir Nasseri, M. Ali Zapp, Daniel Klinikum Rechts der Isar der Technische Universität München Augenklinik und Poliklinik München 81675 Germany Department of Computer Science in Technische Universität München München 85748 Germany Nsk Ltd Japan Eye Hospital Rotterdam Rotterdam Netherlands Johns Hopkins Hospital Wilmer Eye Institute Baltimore MD United States Johns Hopkins University Laboratory for Computational Sensing and Robotics Baltimore MD United States University of Alberta Department of Biomedical Engineering Canada

Advancements in robot-assisted eye surgery have significantly enhanced precision in delicate procedures by enabling controlled movement of surgical instruments through a small entry point (trocar) placed on the outer layer of the eye (sclera). However, before these procedures can begin, the robot must first be prepared, draped, and calibrated. Then, it is positioned near the patient, where precise patient alignment is performed. Finally, the system ensures accurate alignment between the surgical instrument and the trocar, a critical step for safe and effective instrument insertion and surgical execution. Despite these advancements, integrating robotic systems into clinical practice remains challenging. The current manual preparation and alignment process is time-consuming and error-prone, emphasizing the need for a more automated and adaptive solution. Patient alignment time has been identified as a key factor differentiating robot-assisted and manual surgeries in overall procedure duration, with robot preparation and patient alignment extending surgery time by an average of 51 ± 6 minutes compared to manual methods. To address these challenges, this paper introduces a multi-component robotic system combined with an image-guided patient alignment methodology designed to reduce preparation and alignment time in robotic eye surgery while maintaining surgical efficiency. The robotic system consists of a 2D-Cart carrying a 4-degree-of-freedom (DOF) frame robot holding a 5-DOF micro-manipulator. It utilizes an image-guided, multi-stage alignment approach, enabling the system to efficiently be localized and stabilized in an optimal position beside the surgical bed and ensuring precise and adaptive positioning relative to the patient in the surgical draped condition. The proposed end-to-end robotic system preparation and patient alignment are validated through a user study involving eight medical experts in a simulated surgical environment using a draped phantom eye. The r

关键词： Clinical Integration in Medical robotics Medical Robots and Systems Surgical robotics: Planning

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Towards Virtual Clinical Trials of Radiology AI with Conditional Generative Modeling

arXiv

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arXiv 2025年

作者： Killeen, Benjamin D. Wan, Bohua Kulkarni, Aditya V. Drenkow, Nathan Oberst, Michael Yi, Paul H. Unberath, Mathias Department of Computer Science Johns Hopkins University BaltimoreMD21218 United States Laboratory for Computational Sensing and Robotics Johns Hopkins University BaltimoreMD21218 United States Malone Center for Healthcare in Engineering Johns Hopkins University BaltimoreMD21218 United States Department of Radiology St. Jude Children’s Research Hospital 262 Danny Thomas Place MemphisTN38105-3678 United States Applied Physics Laboratory Johns Hopkins University LaurelMD20723 United States

Artificial intelligence and machine learning (AI/ML) are poised to transform healthcare by enabling personalized and efficient patient care through data-driven insights. Although radiology is at the forefront of AI/ML adoption, in practice, the potential of AI/ML models is often overshadowed by severe failures to generalize: AI/ML models can have performance degradation of up to 20% when transitioning from controlled test environments to clinical use by radiologists. This mismatch in advertised and observed AI/ML performance raises concerns that radiologists will be misled by incorrect AI/ML predictions in practice and/or grow to distrust AI/ML, rendering these promising technologies practically ineffectual. Exhaustive clinical trials of AI/ML models throughout the development cycle on abundant and diverse data is thus critical to anticipate AI/ML model degradation when encountering varied data samples. Achieving these goals in practice, however, is challenging due to the high costs of collecting the necessary diverse data samples and the corresponding annotations. To overcome these limitations, we introduce a novel conditional generative AI model designed for virtual clinical trials (VCTs) of radiology AI/ML, capable of realistically synthesizing full-body CT images of patients with specified attributes. By learning the joint distribution of images and anatomical structures, and operating on latent representations for memory efficiency, our model enables precise replication of real-world patient populations with unprecedented detail at this scale. We demonstrate meaningful evaluation of radiology AI models through VCTs powered by our synthetic CT study populations, revealing model degradation and facilitating algorithmic auditing for bias-inducing data attributes. Our generative AI approach to VCTs is a promising avenue towards a scalable solution to assess model robustness, mitigate biases, and safeguard patient care by enabling simpler testing and evaluation of A

关键词： Computerized tomography

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Robotic Ultrasound-Guided Femoral Artery Reconstruction of Anatomically-Representative Phantoms

arXiv

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arXiv 2025年

作者： Al-Zogbi, Lidia Raina, Deepak Pandian, Vinciya Fleiter, Thorsten Krieger, Axel Department of Mechanical Engineering The Laboratory of Computational Sensing and Robotics Johns Hopkins University BaltimoreMD United States Malone Center for Engineering in Healthcare Whiting School of Engineering Johns Hopkins University BaltimoreMD United States Ross and Carol Nese College of Nursing Pennsylvania State University University ParkPA United States R. Cowley Shock Trauma Center Department of Diagnostic Radiology School of Medicine University of Maryland BaltimoreMD United States

Femoral artery access is essential for numerous clinical procedures, including diagnostic angiography, therapeutic catheterization, and emergency interventions. Despite its critical role, successful vascular access remains challenging due to anatomical variability, overlying adipose tissue, and the need for precise ultrasound (US) guidance. Errors in needle placement can lead to severe complications, restricting the procedure to highly skilled clinicians in controlled hospital settings. While robotic systems have shown promise in addressing these challenges through autonomous scanning and vessel reconstruction, clinical translation remains limited due to reliance on simplified phantom models that fail to capture human anatomical complexity. In this work, we present a method for autonomous robotic US scanning of bifurcated femoral arteries, and validate it on five vascular phantoms created from real patient computed tomography (CT) data. Additionally, we introduce a video-based deep learning US segmentation network tailored for vascular imaging, enabling improved 3D arterial reconstruction. The proposed network achieves a Dice score of 89.21% and an Intersection over Union of 80.54% on a newly developed vascular dataset. The quality of the reconstructed artery centerline is evaluated against ground truth CT data, demonstrating an average L2 deviation of 0.91±0.70 mm, with an average Hausdorff distance of 4.36±1.11mm. This study is the first to validate an autonomous robotic system for US scanning of the femoral artery on a diverse set of patient-specific phantoms, introducing a more advanced framework for evaluating robotic performance in vascular imaging and intervention. © 2025, CC BY-NC-ND.

关键词： Phantoms

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