A Fidelity-Embedded Regularization Method for Robust Electrical Impedance Tomography

Kyounghun Lee, Eung Je Woo, Jin Keun Seo

Research output: Contribution to journalArticlepeer-review

37 Citations (Scopus)


Electrical impedance tomography (EIT) provides functional images of an electrical conductivity distribution inside the human body. Since the 1980s, many potential clinical applications have arisen using inexpensive portable EIT devices. EIT acquires multiple trans-impedance measurements across the body from an array of surface electrodes around a chosen imaging slice. The conductivity image reconstruction from the measured data is a fundamentally ill-posed inverse problem notoriously vulnerable to measurement noise and artifacts. Most available methods invert the ill-conditioned sensitivity or the Jacobian matrix using a regularized least-squares data-fitting technique. Their performances rely on the regularization parameter, which controls the trade-off between fidelity and robustness. For clinical applications of EIT, it would be desirable to develop a method achieving consistent performance over various uncertain data, regardless of the choice of the regularization parameter. Based on the analysis of the structure of the Jacobian matrix, we propose a fidelity-embedded regularization (FER) method and a motion artifact reduction filter. Incorporating the Jacobian matrix in the regularization process, the new FER method with the motion artifact reduction filter offers stable reconstructions of high-fidelity images from noisy data by taking a very large regularization parameter value. The proposed method showed practical merits in experimental studies of chest EIT imaging.

Original languageEnglish
Article number8067505
Pages (from-to)1970-1977
Number of pages8
JournalIEEE Transactions on Medical Imaging
Issue number9
Publication statusPublished - 2018 Sept

Bibliographical note

Funding Information:
Manuscript received August 16, 2017; revised October 9, 2017; accepted October 10, 2017. Date of publication October 13, 2017; date of current version August 30, 2018. The work of K. Lee and J. K. Seo was supported by the National Research Foundation of Korea under Grant 2015R1A5A1009350. The work of E. J. Woo was supported by the Korean Health Technology R&D Project under HI14C0743. (Corresponding author: Jin Keun Seo.) K. Lee and J. K. Seo are with the Department of Computational Science and Engineering, Yonsei University, Seoul 03722, South Korea (e-mail: imlkh@yonsei.ac.kr; seoj@yonsei.ac.kr).

Publisher Copyright:
© 2018 IEEE.

All Science Journal Classification (ASJC) codes

  • Software
  • Radiological and Ultrasound Technology
  • Computer Science Applications
  • Electrical and Electronic Engineering


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