Objective: Image-guided surgery provides a mechanism to accurately and quickly assess the location of surgical tools relative to a preoperative image. Traditional image-guided surgery relies on infrared or radiofrequency triangulation to determine an instrument location relative to a preoperative image and has been primarily used in head and neck procedures. Advances in ultrasonic tracking devices, designed for tracking catheters within vessels, may provide an opportunity for image-guided endovascular procedures. This study evaluates the positional accuracy of an ultrasonic navigation system for tracking an endovascular catheter when different stents and graft materials have been deployed in an in vitro system.
Methods: Stent and graft materials commonly used in endovascular procedures were used for this study in combination with a custom three-head ultrasonic transducer navigation system. The stents evaluated were composed of Dacron/nitinol, polytetrafluoroethylene (PTFE)/nitinol, and bare nitinol. They were deployed into excised porcine tissue cannulized with a rotary drill, and a catheter with a custom microtransducer probe was inserted. The distance from each ultrasonic tracking module to a probe mounted on an endovascular catheter was measured using time of flight techniques, and the catheter position in three-dimensions was calculated using triangulation.
Results: The measured position was compared to the actual catheter position determined by a precision translation stage. The PTFE/nitinol, bare nitinol, and Dacron/nitinol stent materials were evaluated and resulted in a maximum error of 1.7, 3.0, and 3.6 mm and an SD of 0.7, 1.2, and 1.4 mm, respectively. A reduction in signal intensity of up to 6x was observed during passage of the endovascular probe through the stent materials, but no reduction in the accuracy of the ultrasonic navigation system was evident.
Conclusion: The use of an ultrasonic-based navigation system is feasible in endovascular procedures, even in the presence of common stent materials. It may have promise as a navigational tool for endovascular procedures.
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http://dx.doi.org/10.1016/j.jvs.2009.07.072 | DOI Listing |
Sensors (Basel)
January 2025
University of Zagreb, Faculty of Transport and Traffic Sciences, Vukelićeva 4, 10000 Zagreb, Croatia.
The possibilities of the Ambient Assisted Living (AAL)/Enhanced Living Environments (ELE) concept in the environment of a smart home were investigated to improve accessibility and improve the quality of life of a person with disabilities. This paper focuses on the concept of predictive information for a person with disabilities in a smart home environment concept where artificial intelligence (AI) and machine learning (ML) systems use data on the user's preferences, habits, and possible incident situations. A conceptual mathematical model is proposed, the purpose of which is to provide predictive user information from defined data sets.
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January 2025
Laboratory of Intelligent Control, Rocket Force University of Engineering, Xi'an 710025, China.
Experts and scholars from various nations have proposed studying low Earth orbit (LEO) satellite signals as the space-based signals of opportunity (SOPs) for navigation and positioning. This method serves as a robust alternative in environments where global navigation satellite systems (GNSS) are unavailable or compromised, providing users with high-precision, anti-interference, secure, and dependable backup navigation solutions. The rapid evolution of LEO communication constellations has spurred the development of SOPs positioning technology using LEO satellites.
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January 2025
Seamless Trans-X Lab (STL), School of Integrated Technology, Yonsei University, Incheon 21983, Republic of Korea.
In the domain of autonomous driving, trajectory prediction plays a pivotal role in ensuring the safety and reliability of autonomous systems, especially when navigating complex environments. Unfortunately, trajectory prediction suffers from uncertainty problems due to the randomness inherent in the driving environment, but uncertainty quantification in trajectory prediction is not widely addressed, and most studies rely on deep ensembles methods. This study presents a novel uncertainty-aware multimodal trajectory prediction (UAMTP) model that quantifies aleatoric and epistemic uncertainties through a single forward inference.
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December 2024
College of Aerospace and Engineering, National University of Defense Technology, Changsha 410073, China.
Due to a short flight time, the dual-axis rotational inertial navigation system carried by some launch vehicles or missiles is often only used for self-calibration and self-alignment. It is generally in the strap-down state rather than the rotation modulation state during flight. This wastes the precision potential of the navigation system.
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December 2024
Electronics Departament, University of Alcalá (UAH), 28805 Alcalá de Henares, Madrid, Spain.
The use of Deep Learning algorithms in the domain of Decision Making for Autonomous Vehicles has garnered significant attention in the literature in recent years, showcasing considerable potential. Nevertheless, most of the solutions proposed by the scientific community encounter difficulties in real-world applications. This paper aims to provide a realistic implementation of a hybrid Decision Making module in an Autonomous Driving stack, integrating the learning capabilities from the experience of Deep Reinforcement Learning algorithms and the reliability of classical methodologies.
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