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Three-dimensional needle-tip localization by electric field potential and camera hybridization for needle electromyography exam robotic simulator

Authors He S, Gómez-Tames J, Yu W

Received 24 October 2015

Accepted for publication 30 March 2016

Published 20 June 2016 Volume 2016:9 Pages 143—150


Checked for plagiarism Yes

Review by Single-blind

Peer reviewer comments 2

Editor who approved publication: Dr Scott Fraser

Siyu He,1 Jose Gomez-Tames,1 Wenwei Yu1,2

1Medical System Engineering Department, Graduate School of Engineering, 2Center for Frontier Medical Engineering, Chiba University, Chiba, Japan

Abstract: As one of neurological tests, needle electromygraphy exam (NEE) plays an important role to evaluate the conditions of nerves and muscles. Neurology interns and novice medical staff need repetitive training to improve their skills in performing the exam. However, no training systems are able to reproduce multiple pathological conditions to simulate real needle electromyogram exam. For the development of a robotic simulator, three components need to be realized: physical modeling of upper limb morphological features, position-dependent electromyogram generation, and needle localization; the latter is the focus of this study. Our idea is to couple two types of sensing mechanism in order to acquire the needle-tip position with high accuracy. One is to segment the needle from camera images and calculate its insertion point on the skin surface by a top-hat transform algorithm. The other is voltage-based depth measurement, in which a conductive tissue-like phantom was used to realize both needle-tip localization and physical sense of needle insertion. For that, a pair of electrodes was designed to generate a near-linear voltage distribution along the depth direction of the tissue-like phantom. The accuracy of the needle-tip position was investigated by the electric field potential and camera hybridization. The results showed that the needle tip could be detected with an accuracy of 1.05±0.57 mm.

needle-tip localization, needle EMG exam, top-hat transform, tissue-like phantom, voltage distribution simulation

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