arXiv subjects

Mohamed E. M. K. Abdelaziz

Publications and source records attributed to Mohamed E. M. K. Abdelaziz.

4 recordsLinked to original sources

arXiv2026

Selectively Actuated Fiberbots for High-precision Minimally Invasive Surgery

Long, slender instruments are essential for minimally invasive surgery (MIS), but achieving precise distal manipulation becomes increasingly difficult when actuation extends along the entire instrument. Here, we present an electrothermally actuated fiber-based robot designed for MIS procedures, in which Joule heating is concentrated in the distal active segment. The fiber is fabricated using a thermal drawing process and uses embedded metallic wires for thermal actuation. This robotic fiber, 1.9 mm in diameter and 1.2 m in length, can precisely manipulate surgical laser fibers with positional precision below 100 $μ$m. The compact size of the fiber robot allows it to pass through the working channel of a flexible endoscope, while keeping both devices mechanically uncoupled to allow independent motion. A phantom demonstration combining the robotic fiber with a da Vinci instrument conceptually demonstrated its compatibility with existing endoscopic systems for clinical applications. Our robotic fiber enables precise distal motion and holds the potential to improve clinical outcomes by enhancing precision in MIS procedures.

cs.RO↗

arXiv2024

A Paradigm Shift in Catheter Development: Thermally Drawn Polymeric Fibers for MR-Guided Cardiovascular Interventions

Cardiovascular diseases (CVDs) and congenital heart diseases (CHD) pose significant global health challenges. Fluoroscopy-guided endovascular interventions, though effective, are accompanied by ionizing radiation concerns, especially in pediatric cases. Magnetic resonance imaging (MRI) emerges as a radiation-free alternative, offering superior soft tissue visualization and functional insights. However, the lack of compatible instruments remains a hurdle. We present two novel catheter systems, a tendon-driven steerable catheter and an active tracking Tiger-shaped catheter, fabricated using a unique fiber drawing technique. These catheters, showcasing mechanical properties similar to commercial counterparts, have undergone rigorous in-vitro and in-vivo testing, yielding promising outcomes. This innovative approach has the potential to streamline medical device development, thus enhancing patient care in MR-guided interventions.

physics.med-ph↗

arXiv2023

CathSim: An Open-source Simulator for Endovascular Intervention

Autonomous robots in endovascular operations have the potential to navigate circulatory systems safely and reliably while decreasing the susceptibility to human errors. However, there are numerous challenges involved with the process of training such robots, such as long training duration and safety issues arising from the interaction between the catheter and the aorta. Recently, endovascular simulators have been employed for medical training but generally do not conform to autonomous catheterization. Furthermore, most current simulators are closed-source, which hinders the collaborative development of safe and reliable autonomous systems. In this work, we introduce CathSim, an open-source simulation environment that accelerates the development of machine learning algorithms for autonomous endovascular navigation. We first simulate the high-fidelity catheter and aorta with a state-of-the-art endovascular robot. We then provide the capability of real-time force sensing between the catheter and the aorta in simulation. Furthermore, we validate our simulator by conducting two different catheterization tasks using two popular reinforcement learning algorithms. The experimental results show that our open-source simulator can mimic the behaviour of real-world endovascular robots and facilitate the development of different autonomous catheterization tasks. Our simulator is publicly available at https://github.com/robotvisionlabs/cathsim.

cs.RO↗

arXiv2020

End-to-End Real-time Catheter Segmentation with Optical Flow-Guided Warping during Endovascular Intervention

Accurate real-time catheter segmentation is an important pre-requisite for robot-assisted endovascular intervention. Most of the existing learning-based methods for catheter segmentation and tracking are only trained on small-scale datasets or synthetic data due to the difficulties of ground-truth annotation. Furthermore, the temporal continuity in intraoperative imaging sequences is not fully utilised. In this paper, we present FW-Net, an end-to-end and real-time deep learning framework for endovascular intervention. The proposed FW-Net has three modules: a segmentation network with encoder-decoder architecture, a flow network to extract optical flow information, and a novel flow-guided warping function to learn the frame-to-frame temporal continuity. We show that by effectively learning temporal continuity, the network can successfully segment and track the catheters in real-time sequences using only raw ground-truth for training. Detailed validation results confirm that our FW-Net outperforms state-of-the-art techniques while achieving real-time performance.

eess.IV↗