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Deep D-bar: Real time Electrical Impedance Tomography Imaging with Deep Neural Networks

Hamilton, SJ; Hauptmann, A; (2018) Deep D-bar: Real time Electrical Impedance Tomography Imaging with Deep Neural Networks. IEEE Transactions on Medical Imaging , 37 (10) pp. 2367-2377. 10.1109/TMI.2018.2828303. Green open access

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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 study, 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.

Type: Article
Title: Deep D-bar: Real time Electrical Impedance Tomography Imaging with Deep Neural Networks
Open access status: An open access version is available from UCL Discovery
DOI: 10.1109/TMI.2018.2828303
Publisher version: http://dx.doi.org/10.1109/TMI.2018.2828303
Language: English
Additional information: © Copyright 2018 IEEE. This work is licensed under a Creative Commons Attribution 3.0 License (http://creativecommons.org/licenses/by/3.0/).
Keywords: electrical impedance tomography, D-bar methods, deep learning, conductivity imaging
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI: https://discovery.ucl.ac.uk/id/eprint/10048808
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