Document Type

Article

Language

eng

Publication Date

10-1-2018

Publisher

Institute of Electrical and Electronic Engineers (IEEE)

Source Publication

IEEE Transactions on Medical Imaging

Source ISSN

0278-0062

Abstract

The mathematical problem for electrical impedance tomography (EIT) is a highly nonlinear ill-posed inverse problem requiring carefully designed reconstruction procedures to ensure reliable image generation. D-bar methods are based on a rigorous mathematical analysis and provide robust direct reconstructions by using a low-pass filtering of the associated nonlinear Fourier data. Similarly to low-pass filtering of linear Fourier data, only using low frequencies in the image recovery process results in blurred images lacking sharp features, such as clear organ boundaries. Convolutional neural networks provide a powerful framework for post-processing such convolved direct reconstructions. In this paper, we demonstrate that these CNN techniques lead to sharp and reliable reconstructions even for the highly nonlinear inverse problem of EIT. The network is trained on data sets of simulated examples and then applied to experimental data without the need to perform an additional transfer training. Results for absolute EIT images are presented using experimental EIT data from the ACT4 and KIT4 EIT systems.

Comments

Accepted version. IEEE Transactions on Medical Imaging, Vol. 37, No. 10 (October 2018): 2367-2377. DOI. © 2018 Institute of Electrical and Electronic Engineers (IEEE). Used with permission.

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