Highly Accurate Image Reconstruction using Electrical Impedance Tomography

Highly Accurate Image Reconstruction using Electrical Impedance Tomography

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This paper proposes a technique for solving the electrical tomography inverse problem. Usually, a set of voltage measurements is acquired from the boundaries of an investigated volume, whilst this is subjected to a sequence of low-frequency current patterns. In principle, measuring both the amplitude and the phase angle of the voltage can result in electrical conductivity images. Image reconstruction in EIT is an inverse problem, commonly presented as minimizing the suitable objective function. The deterministic approach based on Tikhonov regularization was used to obtain the desired conductivity image. To acquire the image of the internal conductivity, we applied the Newton-Raphson method. This iterative procedure is regularly used in the EIT inverse problem for its fast convergence and good reconstruction quality. A level set method was employed in the reconstruction algorithm to ensure accurate results. The outcomes of the described algorithm and the one obtained via standard methods are suitably compared in the article.

This paper proposes a technique for solving the electrical tomography inverse problem. Usually, a set of voltage measurements is acquired from the boundaries of an investigated volume, whilst this is subjected to a sequence of low-frequency current patterns. In principle, measuring both the amplitude and the phase angle of the voltage can result in electrical conductivity images. Image reconstruction in EIT is an inverse problem, commonly presented as minimizing the suitable objective function. The deterministic approach based on Tikhonov regularization was used to obtain the desired conductivity image. To acquire the image of the internal conductivity, we applied the Newton-Raphson method. This iterative procedure is regularly used in the EIT inverse problem for its fast convergence and good reconstruction quality. A level set method was employed in the reconstruction algorithm to ensure accurate results. The outcomes of the described algorithm and the one obtained via standard methods are suitably compared in the article.

EIT, FEM, Regularization, Image Reconstruction

EIT, FEM, Regularization, Image Reconstruction

KŘÍŽ, T.; DUŠEK, J.

2018

22.05.2017

St Petersburg, Russia

978-1-5090-6269-0

PIERS 2017 Proceedings

767

771

5

https://ieeexplore.ieee.org/document/8261845