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Proceedings Paper

Impedance imaging and Markov chain Monte Carlo methods
Author(s): Erkki Somersalo; Jari P. Kaipio; Marko J. Vauhkonen; D. Baroudi; S. Jaervenpaeae
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Paper Abstract

The article discusses the electrical impedance imaging problem (EIT) from a Bayesian point of view. We discuss two essentially different EIT problems: The first one is the static problem of estimating the resistivity distribution of a body from the static current/voltage measurements on the surface of the body. The other problem is a gas temperature distribution retrieval problem by resistivity measurements of metal filaments placed in the gas funnel. In these examples, the prior information contains inequality constraints and non-smooth functionals. Consequently, gradient-based maximum likelihood search algorithms converge poorly. To overcome this difficulty, we study the possibility of using a Markov chain Monte Carlo algorithm to explore the posterior distribution.

Paper Details

Date Published: 9 December 1997
PDF: 11 pages
Proc. SPIE 3171, Computational, Experimental, and Numerical Methods for Solving Ill-Posed Inverse Imaging Problems: Medical and Nonmedical Applications, (9 December 1997); doi: 10.1117/12.279723
Show Author Affiliations
Erkki Somersalo, Helsinki Univ. of Technology (Finland)
Jari P. Kaipio, Univ. of Kuopio (Finland)
Marko J. Vauhkonen, Univ. of Kuopio (Finland)
D. Baroudi, Technical Research Ctr. of Finland (Finland)
S. Jaervenpaeae, Univ. of Helsinki (Finland)


Published in SPIE Proceedings Vol. 3171:
Computational, Experimental, and Numerical Methods for Solving Ill-Posed Inverse Imaging Problems: Medical and Nonmedical Applications
Randall Locke Barbour; Mark J. Carvlin; Michael A. Fiddy, Editor(s)

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