Statistical inversion in electrical impedance tomography using mixed total variation and non-convex lp regularization prior

Thilo Strauss, Taufiquar Khan

Research output: Contribution to journalArticlepeer-review

15 Citations (Scopus)

Abstract

Electrical impedance tomography (EIT) is a well-known technique to estimate the conductivity distribution γ of a body Ω with unknown electromagnetic properties. EIT is a severely ill-posed inverse problem. In this paper, we formulate the EIT problem in the Bayesian framework using mixed total variation (TV) and non-convex lp regularization prior. We use the Markov Chain Monte Carlo (MCMC) method for sampling the posterior distribution to solve the ill-posed inverse problem in EIT. We present simulations to estimate the distribution for each pixel for the image reconstruction of the conductivity in EIT.

Original languageEnglish
Pages (from-to)529-542
Number of pages14
JournalJournal of Inverse and Ill-Posed Problems
Volume23
Issue number5
DOIs
Publication statusPublished - 1 Oct 2015
Externally publishedYes

Keywords

  • EIT
  • electrical impedance tomography
  • inverse problem
  • Markov Chain Monte Carlo method (MCMC)
  • Metropolis-Hastings algorithm
  • mixed TV and l prior
  • non-convex l regularization
  • non-convex prior
  • statistical inversion
  • total variation (TV) prior

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