Effects of electrical impedance measuring methods on two-dimensional tomogram recovery of biological tissues

  • Y.M. Snizhko Oles Honchar Dnipro National University
  • M.M. Milykh Oles Honchar Dnipro National University
  • E.M. Gasanov Oles Honchar Dnipro National University
Keywords: electrical impedance tomography; finite element method; resistivity images; EIDORS; electrodes


The purpose of electrical impedance tomography is to obtain the electrical impedance distribution in the domain of interest by injecting the currents or applying voltages and measuring voltages or currents via a number of electrodes that are mounted on the boundary of the domain. We investigated the influence of various alternating current injection methods on conductivity allocation recovery in biological tissues. We used 16 electrodes allocated uniformly on a circle perimeter. The research technique includes the mathematical modeling by finite element method with 576 nodes. The current injection was performed through two electrodes located nearby (dipole assignment), opposite (polar assignment) or with a shift by 3 electrodes (a quarter of circle). We registered the potential differences between other electrodes for calculation of the internal conductivity allocation by the finite element method. The study revealed that dipole current injection impoved the sensitivity of the method, and polar injection refined the resolution capability. We used the absolute and difference calculation methods implemented in the programming package of potentials allocation and image reconstruction EIDORS (Electrical Impedance and Diffuse Optical Tomography Reconstruction Software). EIDORS is an open source software system for image reconstruction in the electrical impedance tomography and diffuse optical tomography, designed to facilitate collaboration, testing and new research in these fields. Several numerical examples with inclusion of various convex and non-convex smooth shapes (e.g. circular, elliptic, square-shaped) and sizes are presented and thoroughly investigated. The experiments revealed phantoms at round form discontinuities of conductivity. As an accuracy criterion, we selected mean-square and maximum deviation values of the reconstructed image from the true conductivity allocation. The study showed the advantages, lacks and application fields of dipole, polar and other methods of the current injection. The experiments demonstrated the optimal parameters for reconstruction of internal conductivities at various methods of stimulation. The model with polar electrodes showed the best results by the criterion of maximum deviation. The model with electrodes shifted on a circle quarter revealed the best results by mean-square error criterion.


Adler, A., Guardo, R., 1996. Electrical impedance tomography: Regularised imaging and contrast detection. IEEE Trans. Med. Imaging 15, 170–179.
Asfaw, Y., Adler, A., 2005. Automatic detection of detached and erroneous electrodes in electrical impedance tomography. Physiol. Meas. 26(2), S175–S183.
Balleza-Ordaz, M., Perez-Alday, E., Vargas-Luna, M., Riu, J.P., 2015. Tidal volume monitoring by electrical impedance tomography (EIT) using different regions of interest (ROI): Calibration equations. Biomed. Signal Proces. 18(4), 102–109.
Bera, T.K., Nagaraju, J., 2012. Studying the resistivity imaging of chicken tissue phantoms with different current patterns in Electrical Impedance Tomography (EIT). Measurement 45(4), 663–682.
Cao, Z., Wang, H., Yang, W., Yan, Y., 2007. A calculable sensor for electrical impedance tomography. Sensor. Actuat. A-Phys. 140(2), 156–161.
Chakrabarty, D., Chattopadhyay, M., Bhar, R., 2013. Resistivity imaging of a phantom with irregular inhomogeneities with 32 silver electrodes based sensory system in two dimensional electrical impedance tomography. Procedia Technology 10, 191–199.
Cheng, K.-S., Isaacson, D., Newell, J.C., Gisser, D.G., 1989. Electrode models for electric current computed tomography. IEEE Trans. Biomed. Eng. 36, 918–924.
Colton, D., Kress, R., 1992. Inverse acoustic and electromagnetic scattering theory. Springer-Verlag, Berlin.
Javaherian, A., Movafeghi, A., Faghihi, R., Yahaghi, E., 2013. An exhaustive criterion for estimating quality of images in electrical impedance tomography with application to clinical imaging. J. Vis. Commun. Image Representation 24(7), 773–785.
Kim, B.S., Kambhampati, A.K., Jang, Y.J., Kim, K.Y., Kim, S., 2014. Image reconstruction using voltage-current system in electrical impedance tomography. Nucl. Eng. Des. 278(15), 134–140.
Kim, K.Y., Kim, B.S., Kim, M.C., Kim, S., 2004. Dynamic inverse obstacle problems with electrical impedance tomography. Math. Comput. Simulat. 66(4–5), 399–408.
Ledger, P., 2012. hp-Finite element discretisation of the electrical impedance tomography problem. Comput. Method. Appl. M. 225–228, 154–176.
Polydorides, N., Lionheart, W.R.B., 2002. A Matlab toolkit for three-dimensional electrical impedance tomography: A contribution to the electrical impedance and diffuse optical reconstruction software project. Meas. Sci. Technol. 13, 1871–1883.
Zhao, Y., Wang, M., Yao, J., 2014. Electrical impedance tomography spectroscopy method for characterising particles in solid-liquid phase. AIP Conference Proceedings 1592, 10–17.
How to Cite
Snizhko, Y., Milykh, M., & Gasanov, E. (2018). Effects of electrical impedance measuring methods on two-dimensional tomogram recovery of biological tissues. Regulatory Mechanisms in Biosystems, 6(1), 79-83. https://doi.org/10.15421/021515