Image reconstruction for high-contrast conductivity imaging in mutual induction tomography for industrial applications

Manuchehr Soleimani; William R B Lionheart; Anthony J. Peyton

doi:10.1109/TIM.2007.895598

Image reconstruction for high-contrast conductivity imaging in mutual induction tomography for industrial applications

Manuchehr Soleimani, William R B Lionheart, Anthony J. Peyton

Research output: Contribution to journal › Article › peer-review

Abstract

Mutual induction tomography (MIT) attempts to image the electromagnetic characteristics of an object by measuring the mutual inductances between sets of coils placed around its periphery. The application of MIT for molten metal flow visualization is of interest in this paper, which focuses on computational aspects of the forward and inverse MIT problem. The forward problem has been solved using an edge finite element formulation. The Jacobian matrix is a key to image reconstruction in MIT. The entries of the Jacobian matrix are the sensitivity of the measurement data to the image values, which has been generated by an efficient adjoint formulation. We have implemented a standard regularized Gauss-Newton scheme to solve such a problem. The reconstructed images for a high-contrast conductivity example of steel/ argon flow shown in this paper are some of the first nonlinear image reconstruction results for MIT. © 2007 IEEE.

Original language	English
Pages (from-to)	2024-2032
Number of pages	8
Journal	IEEE Transactions on Instrumentation and Measurement
Volume	56
Issue number	5
DOIs	https://doi.org/10.1109/TIM.2007.895598
Publication status	Published - Oct 2007

Keywords

Edge finite element method (FEM)
Industrial process tomography
Inverse boundary value problem
Mutual induction tomography (MIT)
Sensitivity matrix

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10.1109/TIM.2007.895598

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title = "Image reconstruction for high-contrast conductivity imaging in mutual induction tomography for industrial applications",

abstract = "Mutual induction tomography (MIT) attempts to image the electromagnetic characteristics of an object by measuring the mutual inductances between sets of coils placed around its periphery. The application of MIT for molten metal flow visualization is of interest in this paper, which focuses on computational aspects of the forward and inverse MIT problem. The forward problem has been solved using an edge finite element formulation. The Jacobian matrix is a key to image reconstruction in MIT. The entries of the Jacobian matrix are the sensitivity of the measurement data to the image values, which has been generated by an efficient adjoint formulation. We have implemented a standard regularized Gauss-Newton scheme to solve such a problem. The reconstructed images for a high-contrast conductivity example of steel/ argon flow shown in this paper are some of the first nonlinear image reconstruction results for MIT. {\textcopyright} 2007 IEEE.",

keywords = "Edge finite element method (FEM), Industrial process tomography, Inverse boundary value problem, Mutual induction tomography (MIT), Sensitivity matrix",

author = "Manuchehr Soleimani and Lionheart, {William R B} and Peyton, {Anthony J.}",

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KW - Inverse boundary value problem

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KW - Sensitivity matrix

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Image reconstruction for high-contrast conductivity imaging in mutual induction tomography for industrial applications

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Keywords

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