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

He S, Gomez-Tames J, Yu W - Med Devices (Auckl) (2016)

Bottom Line: One is to segment the needle from camera images and calculate its insertion point on the skin surface by a top-hat transform algorithm.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 results showed that the needle tip could be detected with an accuracy of 1.05±0.57 mm.

View Article: PubMed Central - PubMed

Affiliation: Medical System Engineering Department, Graduate School of Engineering.

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.

No MeSH data available.


Related in: MedlinePlus

Image processing error (x,y) using 35 insertion points.Note: Scale bar means the prediction error in mm.
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f7-mder-9-143: Image processing error (x,y) using 35 insertion points.Note: Scale bar means the prediction error in mm.

Mentions: High accuracy of image processing is not only used for the measurement of insertion point but also it allows a more accurate calibration. Figure 7 shows the error of camera image processing at 35 different surface positions, the error was 0.67±0.35 mm. Specifically, in the center area, the errors were generally less than 0.5 mm.


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

He S, Gomez-Tames J, Yu W - Med Devices (Auckl) (2016)

Image processing error (x,y) using 35 insertion points.Note: Scale bar means the prediction error in mm.
© Copyright Policy
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4920258&req=5

f7-mder-9-143: Image processing error (x,y) using 35 insertion points.Note: Scale bar means the prediction error in mm.
Mentions: High accuracy of image processing is not only used for the measurement of insertion point but also it allows a more accurate calibration. Figure 7 shows the error of camera image processing at 35 different surface positions, the error was 0.67±0.35 mm. Specifically, in the center area, the errors were generally less than 0.5 mm.

Bottom Line: One is to segment the needle from camera images and calculate its insertion point on the skin surface by a top-hat transform algorithm.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 results showed that the needle tip could be detected with an accuracy of 1.05±0.57 mm.

View Article: PubMed Central - PubMed

Affiliation: Medical System Engineering Department, Graduate School of Engineering.

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.

No MeSH data available.


Related in: MedlinePlus