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Abstract：
Electrical impedance spectroscopy (EIS) has been proposed as a promising noninvasive method to differentiate healthy thyroid from parathyroid tissues during thyroidectomy. However, previously reported similarities in the in vivo measured spectra of these tissues during a pilot study suggest that this separation may not be straightforward. We utilise computational modelling as a method to elucidate the distinguishing characteristics in the EIS signal and explore the features of the tissue that contribute to the observed electrical behaviour. Firstly, multiscale finite element models (or \'virtual tissue constructs\') of thyroid and parathyroid tissues were developed and verified against in vivo tissue measurements. A global sensitivity analysis was performed to investigate the impact of physiological micro-, meso- and macroscale tissue morphological features of both tissue types on the computed macroscale EIS spectra and explore the separability of the two tissue types. Our results suggest that the presence of a surface fascia layer could obstruct tissue differentiation, but an analysis of the separability of simulated spectra without the surface fascia layer suggests that differentiation of the two tissue types should be possible if this layer is completely removed by the surgeon. Comprehensive in vivo measurements are required to fully determine the potential for EIS as a method in distinguishing between thyroid and parathyroid tissues.
摘要：
已提出电阻抗谱（EIS）作为一种有前途的非侵入性方法，可在甲状腺切除术中区分健康甲状腺与甲状旁腺组织。然而,先前在一项初步研究中报道的这些组织的体内测量光谱的相似性表明，这种分离可能并不简单。我们利用计算建模作为一种方法来阐明EIS信号中的区别特征，并探索有助于观察到的电行为的组织特征。首先,建立了甲状腺和甲状旁腺组织的多尺度有限元模型(或"虚拟组织构造"),并根据体内组织测量结果进行了验证.进行了全局敏感性分析，以研究生理微生物的影响。在计算的宏观EIS光谱上，两种组织类型的中尺度和宏观组织形态特征，并探索两种组织类型的可分离性。我们的结果表明，表面筋膜层的存在可能会阻碍组织分化，但是对没有表面筋膜层的模拟光谱的可分离性的分析表明，如果外科医生完全去除该层，则应该可以区分两种组织类型。需要进行全面的体内测量才能完全确定EIS作为区分甲状腺和甲状旁腺组织的方法的潜力。
