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Monotonicity-Based Regularization for Phantom Experiment Data in Electrical Impedance Tomography

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New Trends in Parameter Identification for Mathematical Models

Part of the book series: Trends in Mathematics ((TM))

Abstract

In electrical impedance tomography, algorithms based on minimizing the linearized-data-fit residuum have been widely used due to their real-time implementation and satisfactory reconstructed images. However, the resulting images usually tend to contain ringing artifacts. In this work, we shall minimize the linearized-data-fit functional with respect to a linear constraint defined by the monotonicity relation in the framework of real electrode setting. Numerical results of standard phantom experiment data confirm that this new algorithm improves the quality of the reconstructed images as well as reduce the ringing artifacts.

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Acknowledgements

The authors thank Professor Eung Je Woo’s EIT research group for the iirc phantom data set. MNM thanks the Academy of Finland (Finnish Center of Excellence in Inverse Problems Research) for financial support of the project number 273979. During part of the preparation of this work, MNM worked at the Department of Mathematics of the Goethe University Frankfurt, Germany.

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Authors and Affiliations

  1. Department of Mathematics, Goethe University Frankfurt, Frankfurt, Germany

    Bastian Harrach

  2. Department of Mathematics and Statistics, University of Helsinki, Helsinki, Finland

    Mach Nguyet Minh

Authors
  1. Bastian Harrach
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  2. Mach Nguyet Minh
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Correspondence to Bastian Harrach .

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Editors and Affiliations

  1. Fakultät für Mathematik, Technische Universität Chemnitz, Chemnitz, Germany

    Bernd Hofmann

  2. Department of Mathematics, Federal University of Santa Catarin, Florianopolis, Brazil

    Antonio LeitĂŁo

  3. Pura e Aplicada, Instituto Nacional de Matematica, Rio de Janeiro, Rio de Janeiro, Brazil

    Jorge P. Zubelli

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Harrach, B., Minh, M.N. (2018). Monotonicity-Based Regularization for Phantom Experiment Data in Electrical Impedance Tomography. In: Hofmann, B., Leitão, A., Zubelli, J. (eds) New Trends in Parameter Identification for Mathematical Models. Trends in Mathematics. Birkhäuser, Cham. https://doi.org/10.1007/978-3-319-70824-9_6

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