Publications by authors named "Hyeuknam Kwon"

Electrical impedance tomography (EIT) is a non-invasive technology that can visualize conductivity changes inside human body in real time using multiple surface electrodes. For convenience of wearing and application, if no electrodes are attached to the back, a commonly used image reconstruction approach produces poor images of dorsal region. In this study, we developed a special current injection and voltage measurement pattern to well reconstruct the conductivity distribution inside the body even in the absence of dorsal electrodes.

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Application of minimally invasive methods to enable the measurement of tissue permittivity in the neuromuscular clinic remain elusive. This paper provides a theoretical and modeling study on the measurement of the permittivity of two-dimensional anisotropic tissues such as skeletal muscle with a multi-electrode cross-shaped needle. For this, we design a novel cross-shaped needle with multiple-electrodes and analyse apparent impedance corresponding to the measured impedance.

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Objective: Electrical impedance myography (EIM) is a recent technology to assess muscle health. As of today, the clinical application of EIM has been applied only to evaluate muscle condition using non-invasive surface electrodes in contact with the skin; however, intermediate tissues at the recording site introduce confounding artifacts which reduce the technique's performance as a biomarker of neuromuscular disorders (NMD). Here, we develop and test in humans a new approach using two concentric needles for intramuscular EIM recordings.

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Conventional magnetic resonance electrical impedance tomography (MREIT) reconstruction methods require administration of two linearly independent currents via at least two electrode pairs. This requires long scanning times and inhibits coordination of MREIT measurements with electrical neuromodulation strategies. We sought to develop an isotropic conductivity reconstruction algorithm in MREIT based on a single current injection, both to decrease scanning time by a factor of two and enable MREIT measurements to be conveniently adapted to general transcranial- or implanted-electrode neurostimulation protocols.

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This paper proposes a deep learning method based on electrical impedance tomography (EIT) to estimate the thickness of abdominal subcutaneous fat. EIT for evaluating the thickness of abdominal subcutaneous fat is an absolute imaging problem that aims at reconstructing conductivity distributions from current-to-voltage data. Existing reconstruction methods based on EIT have difficulty handling the inherent drawbacks of strong nonlinearity and severe ill-posedness of EIT; hence, absolute imaging may not be possible using linearized methods.

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Objective: Electrical impedance myography (EIM) is a relatively new technique to assess neuromuscular disorders (NMD). Although the application of EIM using surface electrodes (sEIM) has been adopted by the neurology community in recent years to evaluate NMD status, sEIM's sensitivity as a biomarker of skeletal muscle condition is impacted by subcutaneous fat (SF) tissue. Here, we develop a method that is able to remove the contribution of SF from sEIM data.

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Objective: To use a standard modeling approach to evaluate the feasibility of imaging healthy and diseased skeletal muscle at the microscopic scale with a novel electrical impedance imaging (EII) needle.

Methods: We modeled an EII needle containing 16 impedance electrodes arranged circumferentially around the shaft of a non-conductive 19-gauge needle in 4 planes. We then combined the finite element method approach with a reconstruction algorithm to create imaging simulations of the electrical properties of the triceps brachii by localized intramuscular fat (as might be seen in any chronic neuromuscular disease) and by localized edema (as in inflammatory myositis or after direct muscle injury).

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Electrical Impedance Tomography (EIT) is a very attractive functional imaging method despite the low sensitivity and resolution. The use of internal electrodes with the conventional reconstruction algorithms was not enough to enhance image resolution and accuracy in the region of interest (ROI). We propose a local ROI imaging method with internal electrodes developed from careful analysis of the sensitivity matrix that is designed to reduce the sensitivity of the voxels outside the local region and optimize the sensitivity of the voxel inside the local region.

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The electrical properties of biological tissues can be described by a complex tensor comprising a simple expression of the effective admittivity. The effective admittivities of biological tissues depend on scale, applied frequency, proportions of extra- and intracellular fluids, and membrane structures. The effective admittivity spectra of biological tissue can be used as a means of characterizing tissue structural information relating to the biological cell suspensions, and therefore measuring the frequency-dependent effective conductivity is important for understanding tissue's physiological conditions and structure.

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