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.
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This work is partially supported by the German Federal Ministry of Education and Research (BMBF) under grant no. 03FH038I3 (MOSES).
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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
- Electrical impedance tomography
- Total generalized variation
- Inverse problem