<abstract xmlns="http://www.w3.org/1999/xhtml">

<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.</p></abstract>

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.

A high accuracy voltage approximation model based on object-oriented sensitivity matrix estimation (OO-SME model) in electrical impedance tomography

Division of Fundamental Engineering, Graduate School of Science and Engineering

Journal of Electrical Bioimpedance

This work is licensed under the Creative Commons Attribution 4.0 International License.

The image reconstruction in electrical impedance tomography (EIT) has low accuracy due to the approximation error between the measured voltage change and the...

A high accuracy voltage approximation model based on object-oriented sensitivity matrix estimation (OO-SME model) in electrical impedance tomography

Pubblicato online: 08 gen 2023

Pagine: 106 - 115

Ricevuto: 22 nov 2022

DOI: https://doi.org/10.2478/joeb-2022-0015

Parole chiave
Electrical impedance tomography, object-oriented sensitivity matrix estimation, high reconstruction accuracy

© 2022 Zengfeng Gao, Panji Nursetia Darma, Daisuke Kawashima, and Masahiro Takei, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

A high accuracy voltage approximation model based on object-oriented sensitivity matrix estimation (OO-SME model) in electrical impedance tomography

Pubblicato online: 08 gen 2023

Pagine: 106 - 115

Ricevuto: 22 nov 2022

DOI: https://doi.org/10.2478/joeb-2022-0015

Parole chiaveElectrical impedance tomography, object-oriented sensitivity matrix estimation, high reconstruction accuracy

© 2022 Zengfeng Gao, Panji Nursetia Darma, Daisuke Kawashima, and Masahiro Takei, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Parole chiave
Electrical impedance tomography, object-oriented sensitivity matrix estimation, high reconstruction accuracy