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The Linearized Inverse Problem in Multifrequency Electrical Impedance Tomography

by G. S. Alberti and H. Ammari and B. Jin and J.-K. Seo and W. Zhang

(Report number 2016-10)

Abstract
This paper provides a mathematical analysis of the linearized inverse problem in multifrequency electrical impedance tomography. We consider the isotropic conductivity distribution with a finite number of unknown inclusions with different frequency dependence, as is often seen in biological tissues. We discuss reconstruction methods for both fully known and partially known spectral profiles, and demonstrate in the latter case the successful employment of difference imaging. We also study the reconstruction with an imperfectly known boundary, and show that the multifrequency approach can eliminate modeling errors and can recover almost all inclusions. In addition, we develop an efficient group sparse recovery algorithm for the robust solution of related linear inverse problems. Several numerical simulations are presented to illustrate and validate the approach.

Keywords: multifrequency electrical impedance tomography, linearized inverse problem, reconstruction, imperfectly known boundary, group sparsity, regularization

@Techreport{AAJSZ16_647,
  author = {G. S. Alberti and H. Ammari and B. Jin and J.-K. Seo and W. Zhang},
  title = {The Linearized Inverse Problem in Multifrequency Electrical Impedance Tomography},
  institution = {Seminar for Applied Mathematics, ETH Z{\"u}rich},
  number = {2016-10},
  address = {Switzerland},
  url = {https://www.sam.math.ethz.ch/sam_reports/reports_final/reports2016/2016-10.pdf },
  year = {2016}
}

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