Modeling and simulation of needle electrical impedance myography in nonhomogeneous isotropic skeletal muscle.

Needle electrical impedance myography (EIM) neuromuscular disorders nonhomogeneous tissue skeletal muscle

Journal

IEEE journal of electromagnetics, RF and microwaves in medicine and biology
ISSN: 2469-7257
Titre abrégé: IEEE J Electromagn RF Microw Med Biol
Pays: United States
ID NLM: 101733288

Informations de publication

Date de publication:
Mar 2022
Historique:
entrez: 18 4 2022
pubmed: 19 4 2022
medline: 19 4 2022
Statut: ppublish

Résumé

Needle electrical impedance myography (EIM) is a recently developed technique for neuromuscular evaluation. Despite its preliminary successful clinical application, further understanding is needed to aid interpreting EIM outcomes in nonhomogeneous skeletal muscle measurements. The framework presented models needle EIM measurements in a bidomain isotropic model. Finite element method (FEM) simulations verify the validity of our model predictions studying two cases: a spherical volume surrounded by tissue and a two-layered tissue. Our models show that EIM is influenced by the vicinity of tissue with different electrical properties. The apparent resistance, reactance and phase relative errors between our theoretical predictions and FEM simulations in the spherical volume case study are ≤0.2%, ≤1.2% and ≤1.0%, respectively. For the two-layered tissue model case study, the relative errors are ≤2%. We propose a bio-physics driven analytical framework describing needle EIM measurements in a nonhomogeneous bidomain tissue model. Our theoretical predictions may lead to new ways for interpreting needle EIM data in neuromuscular diseases that cause compositional changes in muscle content, e.g. connective tissue deposition within the muscle. These changes will manifest themselves by changing the electric properties of the conductor media and will impact impedance values.

Identifiants

pubmed: 35434441
doi: 10.1109/jerm.2021.3091515
pmc: PMC9012450
mid: NIHMS1782286
doi:

Types de publication

Journal Article

Langues

eng

Pagination

103-110

Subventions

Organisme : NINDS NIH HHS
ID : R41 NS112029
Pays : United States

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Auteurs

Xuesong Luo (X)

Department of Automation Science and Electric Engineering, Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, Beihang University, Beijing 100083, China.

Shaoping Wang (S)

Department of Automation Science and Electric Engineering, Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, Beihang University, Beijing 100083, China.

Benjamin Sanchez (B)

Sanchez Research Lab, Department of Electrical and Computer Engineering, Sorenson Molecular Biotechnology Building, 36 South Wasatch Drive, University of Utah, Salt Lake City, UT 84112, USA.

Classifications MeSH