Incorporating structural prior information and sparsity into EIT using parallel level sets

Ville Kolehmainen, Matthias J. Ehrhardt, Simon R. Arridge

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

EIT is a non-linear ill-posed inverse problem which requires sophisticated regularisation techniques to achieve good results. In this paper we consider the use of structural information in the form of edge directions coming from an auxiliary image of the same object being reconstructed. In order to allow for cases where the auxiliary image does not provide complete information we consider in addition a sparsity regularization for the edges appearing in the EIT image. The combination of these approaches is conveniently described through the parallel level sets approach. We present an overview of previous methods for structural regularisation and then provide a variational setting for our approach and explain the numerical implementation. We present results on simulations and experimental data for different cases with accurate and inaccurate prior information. The results demonstrate that the structural prior information improves the reconstruction accuracy, even in cases when there is reasonable uncertainty in the prior about the location of the edges or only partial edge information is available.

Original languageEnglish
Pages (from-to)285-307
Number of pages23
JournalInverse Problems and Imaging
Volume13
Issue number2
Early online date31 Jan 2019
DOIs
Publication statusE-pub ahead of print - 31 Jan 2019

Keywords

  • Computational inverse problem
  • Electrical impedance tomography
  • Finite element method
  • Regularization
  • Structural prior

ASJC Scopus subject areas

  • Analysis
  • Modelling and Simulation
  • Discrete Mathematics and Combinatorics
  • Control and Optimization

Cite this

Incorporating structural prior information and sparsity into EIT using parallel level sets. / Kolehmainen, Ville; Ehrhardt, Matthias J.; Arridge, Simon R.

In: Inverse Problems and Imaging, Vol. 13, No. 2, 31.01.2019, p. 285-307.

Research output: Contribution to journalArticle

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