7 May 2013 Electrical impedance tomography imaging with reduced-order model based on proper orthogonal decomposition
Antti Lipponen, Aku Seppanen, Jari P. Kaipio
Author Affiliations +
Abstract
Electrical impedance tomography (EIT) is an imaging modality in which the conductivity distribution inside a target is reconstructed based on voltage measurements from the target’s surface. Reconstructing the conductivity distribution is known to be an ill-posed inverse problem whose solutions are highly intolerant to modeling errors. To achieve sufficient accuracy, very dense meshes are usually needed in a finite element approximation of the EIT forward model. This leads to very high-dimensional problems and often unacceptably tedious computations for real-time applications. We consider the model reduction in EIT within the Bayesian inversion framework. We construct the reduced-order model by proper orthogonal decompositions (POD) of the electrical conductivity and the potential distributions. The associated POD modes are computed based on a priori information on the conductivity. The feasibility of the reduced-order model is tested both numerically and with experimental data. The proposed approach is shown to speed up EIT reconstruction considerably without significantly decreasing image quality. In the selected test cases, high-quality reconstructions are obtained with the reduced-order model in 3.5% to 5% of the time required for conventional reconstructions.
© 2013 SPIE and IS&T 0091-3286/2013/$25.00 © 2013 SPIE and IS&T
Antti Lipponen, Aku Seppanen, and Jari P. Kaipio "Electrical impedance tomography imaging with reduced-order model based on proper orthogonal decomposition," Journal of Electronic Imaging 22(2), 023008 (7 May 2013). https://doi.org/10.1117/1.JEI.22.2.023008
Published: 7 May 2013
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Cited by 32 scholarly publications.
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KEYWORDS
Electrodes

Tomography

Data modeling

Inverse problems

Error analysis

Finite element methods

Statistical modeling

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