arXiv subjects

Lidia Al-Zogbi

Publications and source records attributed to Lidia Al-Zogbi.

10 recordsLinked to original sources

arXiv2026

Differentiable Mesh State Estimation via Factor Graph Inference for Deformable Object Reconstruction

Estimating deformable object states remains a fundamental challenge in robotics and simulation. We propose a novel factor graph-based framework for probabilistic mesh state estimation of deformable objects. The method directly updates a tetrahedral mesh, a rich and physically-grounded representation of an environment, by combining physics priors, noisy sensor measurements, and temporal smoothness constraints within a unified probabilistic formulation. The estimation problem is posed as a nonlinear least-squares optimization and solved using Levenberg-Marquardt. Ex vivo central-airway obstruction experiments and simulations on deforming cube models demonstrate reliable and accurate reconstruction under both rigid motion and deformation, highlighting the potential of this probabilistic approach for principled, measurement-driven mesh state estimation in deformable object reconstruction.

cs.RO↗

arXiv2026

PERSEUS: Perception with Semantic Endoscopic Understanding and SLAM

Purpose: Natural orifice surgeries minimize the need for incisions and reduce the recovery time compared to open surgery; however, they require a higher level of expertise due to visualization and orientation challenges. We propose a perception pipeline for these surgeries that allows semantic scene understanding. Methods: We bring learning-based segmentation, depth estimation, and 3D reconstruction modules together to create real-time segmented maps of the surgical scenes. Additionally, we use registration with robot poses to solve the scale ambiguity of mapping from monocular images, and allow the use of semantically informed real-time reconstructions in robotic surgeries. Results: We achieve sub-milimeter reconstruction accuracy based on average one-sided Chamfer distances, average pose registration RMSE of 0.9 mm, and an estimated scale within 2% of ground truth. Conclusion: We present a modular perception pipeline, integrating semantic segmentation with real-time monocular SLAM for natural orifice surgeries. This pipeline offers a promising solution for scene understanding that can facilitate automation or surgeon guidance.

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arXiv2026

AURA-CVC: Autonomous Ultrasound-guided Robotic Assistance for Central Venous Catheterization

Purpose: Central venous catheterization (CVC) is a critical medical procedure for vascular access, hemodynamic monitoring, and life-saving interventions. Its success remains challenging due to the need for continuous ultrasound-guided visualization of a target vessel and approaching needle, which is further complicated by anatomical variability and operator dependency. Errors in needle placement can lead to life-threatening complications. While robotic systems offer a potential solution, achieving full autonomy remains challenging. In this work, we propose an end-to-end robotic-ultrasound-guided CVC pipeline, from scan initialization to needle insertion. Methods: We introduce a deep-learning model to identify clinically relevant anatomical landmarks from a depth image of the patient's neck, obtained using RGB-D camera, to autonomously define the scanning region and paths. Then, a robot motion planning framework is proposed to scan, segment, reconstruct, and localize vessels (veins and arteries), followed by the identification of the optimal insertion zone. Finally, a needle guidance module plans the insertion under ultrasound guidance with operator's feedback. This pipeline was validated on a high-fidelity commercial phantom across 10 simulated clinical scenarios. Results: The proposed pipeline achieved 10 out of 10 successful needle placements on the first attempt. Vessels were reconstructed with a mean error of 2.15 \textit{mm}, and autonomous needle insertion was performed with an error less than or close to 1 \textit{mm}. Conclusion: To our knowledge, this is the first robotic CVC system demonstrated on a high-fidelity phantom with integrated planning, scanning, and insertion. Experimental results show its potential for clinical translation.

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arXiv2025

SlicerROS2: A Research and Development Module for Image-Guided Robotic Interventions

Image-guided robotic interventions involve the use of medical imaging in tandem with robotics. SlicerROS2 is a software module that combines 3D Slicer and robot operating system (ROS) in pursuit of a standard integration approach for medical robotics research. The first release of SlicerROS2 demonstrated the feasibility of using the C++ API from 3D Slicer and ROS to load and visualize robots in real time. Since this initial release, we've rewritten and redesigned the module to offer greater modularity, access to low-level features, access to 3D Slicer's Python API, and better data transfer protocols. In this paper, we introduce this new design as well as four applications that leverage the core functionalities of SlicerROS2 in realistic image-guided robotics scenarios.

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arXiv2025

Sensorless Remote Center of Motion Misalignment Estimation

Laparoscopic surgery constrains instrument motion around a fixed pivot point at the incision into a patient to minimize tissue trauma. Surgical robots achieve this through either hardware to software-based remote center of motion (RCM) constraints. However, accurate RCM alignment is difficult due to manual trocar placement, patient motion, and tissue deformation. Misalignment between the robot's RCM point and the patient incision site can cause unsafe forces at the incision site. This paper presents a sensorless force estimation-based framework for dynamically assessing and optimizing RCM misalignment in robotic surgery. Our experiments demonstrate that misalignment exceeding 20 mm can generate large enough forces to potentially damage tissue, emphasizing the need for precise RCM positioning. For misalignment $D\geq $ 20 mm, our optimization algorithm estimates the RCM offset with an absolute error within 5 mm. Accurate RCM misalignment estimation is a step toward automated RCM misalignment compensation, enhancing safety and reducing tissue damage in robotic-assisted laparoscopic surgery.

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arXiv2025

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

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. Needle placement errors can result in severe complications, thereby limiting the procedure to highly skilled clinicians operating in controlled hospital environments. 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 new vascular dataset. The reconstructed artery centerline is evaluated against ground truth CT data, showing an average L2 error 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.

cs.RO↗

arXiv2025

From Monocular Vision to Autonomous Action: Guiding Tumor Resection via 3D Reconstruction

Surgical automation requires precise guidance and understanding of the scene. Current methods in the literature rely on bulky depth cameras to create maps of the anatomy, however this does not translate well to space-limited clinical applications. Monocular cameras are small and allow minimally invasive surgeries in tight spaces but additional processing is required to generate 3D scene understanding. We propose a 3D mapping pipeline that uses only RGB images to create segmented point clouds of the target anatomy. To ensure the most precise reconstruction, we compare different structure from motion algorithms' performance on mapping the central airway obstructions, and test the pipeline on a downstream task of tumor resection. In several metrics, including post-procedure tissue model evaluation, our pipeline performs comparably to RGB-D cameras and, in some cases, even surpasses their performance. These promising results demonstrate that automation guidance can be achieved in minimally invasive procedures with monocular cameras. This study is a step toward the complete autonomy of surgical robots.

cs.CV↗

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.

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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.

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arXiv2021

Positive Pressure Testing Booths Development and Deployment In Response To The COVID-19 Outbreak

The COVID-19 pandemic left an unprecedented impact on the general public health, resulting in thousands of deaths in the US alone. Nationwide testing plans were initiated to control the spread, with drive-through being the currently dominant testing approach, which, however, exhausts personal protective equipment supplies, and is unfriendly to individuals not owning a vehicle. Walk-up testing booths are a safe alternative, but are too prohibitively priced on the market to allow for nationwide deployment. In this paper, we present an accessible, mobile, affordable, and safe version of a positive-pressure COVID-19 testing booth. The booths are manufactured using primarily off-the-shelf components from US vendors with minimized customization. The booths' mobility allows them to be easily transported within local communities to test a larger subset of the population with fewer transportation options. Moreover, the final bill of materials does not surpass USD 3,900, which is about half of the market price. The booths are air conditioned and HEPA filtered to offer healthcare providers a safe and comfortable working environment. The prototype passed required pressure and air exchange tests, and was positively reviewed by two healthcare professionals. Currently, five booths are deployed and used at the Johns Hopkins University School of Nursing, Baltimore City Health Department, and two community health centers in Baltimore. Our design facilitates walk-up testing in the US, as it decreases PPE consumption; reduces the risk of infection; and is accessible to lower-income communities and non-drivers.

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