文章基本信息

标题：Electro-Mechanical Coupling in Impedance-Based Tissue Differentiation Under Compression*
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作者：Carina Veil ; Sandra Schöne ; Niklas Harland 等
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2022
卷号：55
期号：20
页码：564-569
DOI：10.1016/j.ifacol.2022.09.155
语种：English
出版社：Elsevier
摘要：AbstractImpedance spectroscopy is a useful diagnostic tool in oncology to determine malignant and healthy tissue areas. However, since the electrical measurements are sensitive enough to detect structural changes in tissue, they are also prone to exterior influences such as the contact force of the sensor and mechanical stress in the tissue. These disturbances might predominate electrical properties and make tissue differentiation difficult. In this work, a multi-physical model for the electro-mechanical coupling during impedance spectroscopy is established. Increasing resistivity at rising compression levels is an often observed phenomena that can be explained by the extrusion of fluids from the compressed tissue. With fluids being important ion carriers, tissue conductivity heavily depends on the fluid content within the sample. Separate electrical and mechanical models for tissue are identified and linked through the fluid volume and its transfer to neighboring structures under compression. Measurements on both healthy urinary bladder wall and bladder tumors are carried out to identify the model parameters and to investigate if the changes induced by mechanical stress outweigh the pathological changes. The recorded data fits the electro-mechanical model and confirms that the impedance variations under compression originate from the fluid exodus. Furthermore, the electrical parameters for extracellular matrix resistivity and membrane capacity are found to differ significantly between healthy and malignant tissue, yielding to the conclusion that the apprehension of electromechanical factors prevailing over pathological changes is ungrounded for these parameters.
关键词：KeywordsElectrical impedance spectroscopytissue differentiationphysical modelsbiomedical systems