川嶋 大介

Non-invasive hERG channel screening is achieved by integrating electrical impedance tomography (EIT) and extracellular voltage activation (EVA) into a PCB sensor.

<jats:title>Abstract</jats:title> <jats:p>Image reconstruction in electrical impedance tomography (EIT) is a typical ill-posed inverse problem, from which the stability of conductivity reconstruction affects the reliability of physiological parameters evaluation. In order to improve the stability, the effect of boundary voltage noise on conductivity reconstruction should be controlled. A noise-controlling method based on hybrid current-stimulation and voltage-measurement for EIT (HCSVM-EIT) is proposed for stable conductivity reconstruction. In HCSVM-EIT, the boundary voltage is measured by one current-stimulation and voltage-measurement pattern (high-SNR pattern) with a higher signal-to-noise ratio (SNR); the sensitivity matrix is calculated by another current-stimulation and voltage-measurement pattern (low-cond pattern) with a lower condition number; the boundary voltage is then transformed from the high-SNR pattern into the low-cond pattern by multiplying by an optimized transformation matrix for image reconstruction. The stability of conductivity reconstruction is improved by combining the advantages of the high-SNR pattern for boundary voltage measurement and the low-cond pattern for sensitivity matrix calculation. The simulation results show that the HCSVM-EIT increases the correlation coefficient (CC) of conductivity reconstruction. The experiment results show that the CC of conductivity reconstruction of the human lower limb is increased from 0.3424 to 0.5580 by 62.97% compared to the quasi-adjacent pattern, and from 0.4942 to 0.5580 by 12.91% compared to the adjacent pattern. In conclusion, the stable conductivity reconstruction with higher CC in HCSVM-EIT improves the reliability of physiological parameters evaluation for disease detection.</jats:p>

Abstract The assessment method of anisotropic transmembrane transport coefficient vector P of a cell-spheroid under inhomogeneous ion concentration fields has been proposed by combining electrical impedance tomography (EIT) with an ion transport model to evaluate the anisotropic transmembrane transport of ions. An element P_i of P represents the transmembrane transport coefficient of the ith part of the cell membrane, which is assessed by the ion transport model from the average conductivity σ̃_i of the ith extracellular sector reconstructed by EIT. Anisotropic factor H obtained from P_i is introduced, which represents the anisotropic transmembrane transport. To validate our methodology, the inhomogeneous ion concentration fields are generated by injecting two tonicity-different sucrose solutions (isotonic, hypotonic or hypertonic) from both sides of the cell-spheroid. As a result, the inhomogeneous ion concentration distribution due to the anisotropic transmembrane transport is successfully observed from the reconstructed image by EIT. The anisotropic factor H shows that H = 0.34 ± 0.24 in isotonic and hypertonic combination, H = 0.58 ± 0.15 in isotonic and hypotonic combination and H = 0.23 ± 0.06 in hypertonic and hypotonic combination, respectively. To verify the results obtained by our methodology, the fluorescence ratio F [-] of potassium ions around the cell-spheroid is observed under three combinations as same as the EIT measurement. F shows the anisotropic transmembrane transport with the same trend with the EIT results.

<jats:title>Abstract</jats:title> <jats:p>The image reconstruction in electrical impedance tomography (EIT) has low accuracy due to the approximation error between the measured voltage change and the approximated voltage change, from which the object cannot be accurately reconstructed and quantitatively evaluated. A voltage approximation model based on object-oriented sensitivity matrix estimation (OO-SME model) is proposed to reconstruct the image with high accuracy. In the OO-SME model, a sensitivity matrix of the object-field is estimated, and the sensitivity matrix change from the background-field to the object-field is estimated to optimize the approximated voltage change, from which the approximation error is eliminated to improve the reconstruction accuracy. Against the existing linear and nonlinear models, the approximation error in the OO-SME model is eliminated, thus an image with higher accuracy is reconstructed. The simulation shows that the OO-SME model reconstructs a more accurate image than the existing models for quantitative evaluation. The relative accuracy (RA) of reconstructed conductivity is increased up to 83.98% on average. The experiment of lean meat mass evaluation shows that the RA of lean meat mass is increased from 7.70% with the linear model to 54.60% with the OO-SME model. It is concluded that the OO-SME model reconstructs a more accurate image to evaluate the object quantitatively than the existing models.</jats:p>