Higher order total variation regularization for EIT reconstruction

Abstract

Electrical impedance tomography (EIT) attempts to reveal the conductivity distribution of a domain based on the electrical boundary condition. This is an ill-posed inverse problem; its solution is very unstable. Total variation (TV) regularization is one of the techniques commonly employed to stabilize reconstructions. However, it is well known that TV regularization induces staircase effects, which are not realistic in clinical applications. To reduce such artifacts, modified TV regularization terms considering a higher order differential operator were developed in several previous studies. One of them is called total generalized variation (TGV) regularization. TGV regularization has been successively applied in image processing in a regular grid context. In this study, we adapted TGV regularization to the finite element model (FEM) framework for EIT reconstruction. Reconstructions using simulation and clinical data were performed. First results indicate that, in comparison to TV regularization, TGV regularization promotes more realistic images.

Reconstructed conductivity changes located on selected vertical lines. For each of the reconstructed images as well as the ground truth image, conductivity changes located along the selected left and right vertical lines are plotted. In these plots, the notation GT in the legend stands for ground truth, TV stands for total variation method, and TGV stands for total generalized variation method. Reconstructed conductivity distributions from the GREIT algorithm are also demonstrated.

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Funding

This work is partially supported by the German Federal Ministry of Education and Research (BMBF) under grant no. 03FH038I3 (MOSES).

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Correspondence to Bo Gong.

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Gong, B., Schullcke, B., Krueger-Ziolek, S. et al. Higher order total variation regularization for EIT reconstruction. Med Biol Eng Comput 56, 1367–1378 (2018). https://doi.org/10.1007/s11517-017-1782-z

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Keywords

  • Electrical impedance tomography
  • Total generalized variation
  • Inverse problem