A New Divide-and-Conquer Method for 3-D Electrical Impedance Tomography

Sebastien Martin, T.m. Choi*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

Electrical impedance tomography (EIT) is a patient-safe approach for imaging applications and is a promising technology for biomedical imaging. By applying a small electrical current to living tissue and measuring the electrical potential at different points of the tissue's boundary, it is possible to solve the inverse problem and to generate a map of the tissue's conductivities. Although it is a promising method, the reconstruction process remains a complicated task, and real-time nonlinear image reconstruction algorithms have not yet been widely adopted. Recent studies have proposed the use of artificial neural networks (ANN) to solve the EIT inverse problem. ANNs can give a nonlinear conductivity distribution. Although several studies have examined 2-D finite-element models, very little work has been done on 3-D problems. In this paper, a solution based on the divide-and-conquer method and ANNs is used to solve the nonlinear inverse problem without any linearization. The solution presented here reduces the difficulty of training large ANNs, which are commonly required to solve a 3-D EIT problem with artificial intelligence algorithms.

Original languageEnglish
Article number5100404
JournalIEEE Transactions on Magnetics
Volume54
Issue number3
DOIs
StatePublished - 1 Mar 2018

Keywords

  • Artificial neural networks (ANN)
  • complexity
  • electrical capacitance tomography
  • inverse problems

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