Quantification of measurement error effects on conductivity reconstruction in electrical impedance tomography

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3 Citations (Scopus)


Electrical impedance tomography (EIT) is a boundary measurement inverse technique targeting reconstruction of the conductivity distribution of the interior of a physical body based on boundary measurement data. Typically, the measured data are uncertain because of various error sources; thus, there are many uncertainties in the reconstructed image. This study attempts to quantify the effects of these measurement errors on EIT reconstruction. A comprehensive framework that combines uncertainty quantification techniques and EIT reconstruction techniques is proposed. In this framework, a polynomial chaos expansion method is used to construct a surrogate model of the conductivity field with respect to the measurement errors. Two shape detection indices are introduced to show the EIT reconstruction quality. Finally, under certain detection index constraints, statistical and sensitivity analyses are performed using the properties of the surrogate model. Several EIT problems are examined in this study, involving one or two anomalies in a circular domain or two asymmetric anomalies in a body-like domain. The results show that the proposed framework can quantify the effects of measurement errors on EIT reconstruction at reasonable cost. Further, for the test cases, the measurement errors at the electrodes close to the anomalies are shown to have the greatest influence on the image reconstruction.

Original languageEnglish
Pages (from-to)1669-1693
Number of pages25
JournalInverse Problems in Science and Engineering
Issue number12
Publication statusPublished - 2020 Dec

Bibliographical note

Funding Information:
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (Ministry of Science and ICT) (NRF-2017R1E1A1A0-3070161 and NRF-20151009350).

Publisher Copyright:
© 2020 Informa UK Limited, trading as Taylor & Francis Group.

All Science Journal Classification (ASJC) codes

  • Engineering(all)
  • Computer Science Applications
  • Applied Mathematics


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