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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

Average needle-penetration distance (z) from the voltage distribution of the phantom before and after voltage calibration: (A) All measurement results and (B) measurement in center region.
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f6-mder-9-143: Average needle-penetration distance (z) from the voltage distribution of the phantom before and after voltage calibration: (A) All measurement results and (B) measurement in center region.

Mentions: The estimated needle penetration depth before and after calibration is shown in Figure 6. Before calibration, the results had a huge deviation with respect to the expected values. In Figure 6A, the error of no calibration was 4.11±1.05 mm. In calibration 1, we used the conversion equation from the simulation at the center point to calibrate all insertion points, and the result was 1.36±0.90 mm. In calibration 2, we used conversion equations corresponding to the insertion point, which resulted in 1.08±0.58 mm of error. Figure 6B shows only the result in the center region (circle points denoted in Figure 5) as follows: no calibration, 4.26±0.88 mm; calibration 1, 1.15±0.86 mm; and calibration 2, 0.80±0.68 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)

Average needle-penetration distance (z) from the voltage distribution of the phantom before and after voltage calibration: (A) All measurement results and (B) measurement in center region.
© Copyright Policy
Related In: Results  -  Collection

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

f6-mder-9-143: Average needle-penetration distance (z) from the voltage distribution of the phantom before and after voltage calibration: (A) All measurement results and (B) measurement in center region.
Mentions: The estimated needle penetration depth before and after calibration is shown in Figure 6. Before calibration, the results had a huge deviation with respect to the expected values. In Figure 6A, the error of no calibration was 4.11±1.05 mm. In calibration 1, we used the conversion equation from the simulation at the center point to calibrate all insertion points, and the result was 1.36±0.90 mm. In calibration 2, we used conversion equations corresponding to the insertion point, which resulted in 1.08±0.58 mm of error. Figure 6B shows only the result in the center region (circle points denoted in Figure 5) as follows: no calibration, 4.26±0.88 mm; calibration 1, 1.15±0.86 mm; and calibration 2, 0.80±0.68 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