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Sensing Technologies for Guidance during Needle-based Interventions

Cheng, Zhuoqi; Koskinopoulou, Maria; Bano, Sophia; Stoyanov, Danail; Savarimuthu, Thiusius Rajeeth; Mattos, Leonardo S; (2024) Sensing Technologies for Guidance during Needle-based Interventions. IEEE Transactions on Instrumentation and Measurement 10.1109/tim.2024.3441017. (In press). Green open access

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Abstract

Needle intervention is widely employed in clinical practices such as biopsies, regional anesthesia, blood sampling, neurosurgery, and brachytherapy. Traditional needle insertion relies on surgeon expertise and kinesthetic feedback, yet accurately targeting deep tissue structures remains challenging. To address this, significant research has advanced sensing technologies to aid insertion accuracy. This paper comprehensively reviews recent developments in needle insertion sensing techniques, encompassing needle tip position tracking, proximity measurement, and puncture detection. It evaluates these methods across metrics including accuracy, cost-effectiveness, portability, compatibility, noise resistance capability, Technology Readiness Level (TRL), and future trends. Emerging research directions highlight advancements in machine learning integration, miniaturization, and enhanced multimodal sensing capabilities to improve procedural outcomes and expand application domains.

Type: Article
Title: Sensing Technologies for Guidance during Needle-based Interventions
Open access status: An open access version is available from UCL Discovery
DOI: 10.1109/tim.2024.3441017
Publisher version: https://doi.org/10.1109/tim.2024.3441017
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Biomedical sensor, needle tip tracking, proximity sensing, puncture detection, image-guided insertion
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI: https://discovery.ucl.ac.uk/id/eprint/10195976
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