The DOI matches its Crossref record; 14 of the 14 references it cites were found in a registry or an index.
Deep D-Bar: Real-Time Electrical Impedance Tomography Imaging With Deep Neural Networks
Open accessAbstract
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.
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Cites
U-Net: Convolutional Networks for Biomedical Image Segmentation
Ronneberger, O., Fischer, P., & Brox, T. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24574-4_28 · Cited by 99,404
Deep Convolutional Neural Network for Inverse Problems in Imaging
Jin, K. H., McCann, M. T., Froustey, E., & Unser, M. (2017) · IEEE Transactions on Image Processing · doi:10.1109/tip.2017.2713099 · Cited by 2,529 · cited 5×
“This successful transition to experimental data underlines the robustness of the D-bar algorithm and is especially important as the need for good training data is often the bottleneck for the success of such network-based approaches for other imaging modalities, [14], [18], [19].”
BackgroundMethodResultInfluentialA deep convolutional neural network using directional wavelets for low‐dose X‐ray CT reconstruction
Kang, E., Min, J., & Ye, J. C. (2017) · Medical Physics · doi:10.1002/mp.12344 · Cited by 709
Linear and Nonlinear Inverse Problems with Practical Applications
Mueller, J. L., & Siltanen, S. (2012) · Society for Industrial and Applied Mathematics eBooks · doi:10.1137/1.9781611972344 · Cited by 529
Solving ill-posed inverse problems using iterative deep neural networks
Adler, J., & Öktem, O. (2017) · Inverse Problems · doi:10.1088/1361-6420/aa9581 · Cited by 529
A Mathematical Theory of Deep Convolutional Neural Networks for Feature Extraction
Wiatowski, T., & Bolcskei, H. (2018) · IEEE Transactions on Information Theory · doi:10.1109/tit.2017.2776228 · Cited by 399
Model-Based Learning for Accelerated, Limited-View 3-D Photoacoustic Tomography
Hauptmann, A., Lucka, F., Betcke, M., Huynh, N., Adler, J., Cox, B., Beard, P., Ourselin, S., & Arridge, S. (2018) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2018.2820382 · Cited by 339
Self-citationDeep learning for photoacoustic tomography from sparse data
Antholzer, S., Haltmeier, M., & Schwab, J. (2019) · Inverse Problems in Science and Engineering · doi:10.1080/17415977.2018.1518444 · Cited by 301
Imaging in acute respiratory distress syndrome
Pesenti, A. M., Musch, G., Lichtenstein, D. A., Mojoli, F., Amato, M. B. P., Cinnella, G., Gattinoni, L., & Quintel, M. (2016) · Intensive Care Medicine · doi:10.1007/s00134-016-4328-1 · Cited by 151
Compensation of Modelling Errors Due to Unknown Domain Boundary in Electrical Impedance Tomography
Nissinen, A., Kolehmainen, V., & Kaipio, J. P. (2010) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2010.2073716 · Cited by 128
Metadata
- Authors
- Hamilton, S. J., & Hauptmann, A.
- Venue
- IEEE Transactions on Medical Imaging
- Published
- 2018-10
- Volume
- 37
- Issue
- 10
- Pages
- 2367-2377
- ISSN
- 0278-0062 (ISSN-L)
- 1558-254X
- OpenAlex
- W2767248316
- Cited by
- 365
- References
- 14
- FWCI
- 11.48
- Type
- journal article
- Language
- en
- Open access
- hybrid
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