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Fisher information analysis in electrical impedance tomography
Linnaeus University, Faculty of Technology, Department of Physics and Electrical Engineering. Lund University.ORCID iD: 0000-0002-7018-6248
Lund University.
Linnaeus University, Faculty of Technology, Department of Mathematics.
Linnaeus University, Faculty of Technology, Department of Mathematics.
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2013 (English)In: Journal of Geophysics and Engineering, ISSN 1742-2132, E-ISSN 1742-2140, Vol. 10, no 6, Article ID: 064008- p.Article in journal (Refereed) Published
Abstract [en]

This paper provides a quantitative analysis of the optimal accuracy and resolution in electrical impedance tomography (EIT) based on the Cramér–Rao lower bound. The imaging problem is characterized by the forward operator and its Jacobian. The Fisher information operator is defined for a deterministic parameter in a real Hilbert space and a stochastic measurement in a finite-dimensional complex Hilbert space with a Gaussian measure. The connection between the Fisher information and the singular value decomposition (SVD) based on the maximum likelihood (ML) criterion (the ML-based SVD) is established. It is shown that the eigenspaces of the Fisher information provide a suitable basis to quantify the trade-off between the accuracy and the resolution of the (nonlinear) inverse problem. It is also shown that the truncated ML-based pseudo-inverse is a suitable regularization strategy for a linearized problem, which exploits sufficient statistics for estimation within these subspaces. The statistical-based Cramér–Rao lower bound provides a complement to the deterministic upper bounds and the L-curve techniques that are employed with linearized inversion. To this end, electrical impedance tomography provides an interesting example where the eigenvalues of the SVD usually do not exhibit a very sharp cut-off, and a trade-off between the accuracy and the resolution may be of practical importance. A numerical study of a hypothetical EIT problem is described, including a statistical analysis of the model errors due to the linearization.

Place, publisher, year, edition, pages
Institute of Physics (IOP), 2013. Vol. 10, no 6, Article ID: 064008- p.
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Research subject
Physics, Waves and Signals
Identifiers
URN: urn:nbn:se:lnu:diva-27493DOI: 10.1088/1742-2132/10/6/064008ISI: 000334994600009OAI: oai:DiVA.org:lnu-27493DiVA: diva2:636311
Available from: 2013-07-09 Created: 2013-07-09 Last updated: 2017-01-10Bibliographically approved

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Nordebo, SvenNilsson, BörjeSjödén, Therese
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CiteExportLink to record
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Citation style
  • apa
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  • vancouver
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