用于肌电阻抗成像的表面电极配置的灵敏度分布模拟。
Sensitivity distribution simulations of surface electrode configurations for electrical impedance myography.
作者信息
Rutkove Seward B, Pacheck Adam, Sanchez Benjamin
机构信息
Department of Neurology, Beth Israel Deaconess Medical Center, Harvard Medical School, DA-0730A, 330 Brookline Avenue, Boston, Masachusetts, 02215-5491, USA.
出版信息
Muscle Nerve. 2017 Nov;56(5):887-895. doi: 10.1002/mus.25561. Epub 2017 Mar 21.
INTRODUCTION
Surface-based electrical impedance myography (EIM) is sensitive to muscle condition in neuromuscular disorders. However, the specific contribution of muscle to the obtained EIM values is unknown.
METHODS
We combined theory and the finite element method to calculate the electrical current distribution in a 3-dimensional model using different electrode array designs and subcutaneous fat thicknesses (SFTs). Through a sensitivity analysis, we decoupled the contribution of muscle from other surrounding tissues in the measured surface impedance values.
RESULTS
The contribution of muscle to surface EIM values varied greatly depending on the electrode array size and the SFT. For example, the contribution of muscle with 6-mm SFT was 8% for a small array compared with 32% for a large array.
CONCLUSIONS
The approach presented can be employed to inform the design of robust EIM electrode configurations that maximize the contribution of muscle across the disease and injury spectrum. Muscle Nerve 56: 887-895, 2017.
引言
基于表面的电阻抗肌电图（EIM）对神经肌肉疾病中的肌肉状况敏感。然而，肌肉对所获得的EIM值的具体贡献尚不清楚。
方法
我们结合理论和有限元方法，使用不同的电极阵列设计和皮下脂肪厚度（SFT）来计算三维模型中的电流分布。通过敏感性分析，我们在测量的表面阻抗值中分离出肌肉与其他周围组织的贡献。
结果
肌肉对表面EIM值的贡献因电极阵列大小和SFT的不同而有很大差异。例如，对于6毫米SFT的肌肉，小阵列的贡献为8%，而大阵列的贡献为32%。
结论
所提出的方法可用于指导设计强大的EIM电极配置，以在疾病和损伤范围内最大化肌肉的贡献。《肌肉与神经》56: 887 - 895, 2017年。
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