Name
Abstract | ||
|---|---|---|
Objective: Deep learning has recently been applied to electrical impedance tomography (EIT) imaging. Nevertheless, there are still many challenges that this approach has to face, e.g., targets with sharp corners or edges cannot be well recovered when using circular inclusion training data. This paper proposes an iterative-based inversion method and a convolutional neural network (CNN) based invers... |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/TBME.2019.2891676 | IEEE Transactions on Biomedical Engineering |
Keywords | Field | DocType |
Tomography,Electrodes,Conductivity,Deep learning,Image reconstruction,Mathematical model | Iterative reconstruction,Computer vision,Subspace topology,Computer science,Convolutional neural network,Iterative method,Algorithm,Total variation denoising,Artificial intelligence,Deep learning,Ellipse,Electrical impedance tomography | Journal |
Volume | Issue | ISSN |
66 | 9 | 0018-9294 |
Citations | PageRank | References |
4 | 0.43 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhun Wei | 1 | 5 | 0.80 |
| Dong Liu | 2 | 12 | 5.34 |
| Xudong Chen | 3 | 15 | 1.66 |