Abstract
Recently, methods based on deep learning have been introduced to the literature as a solution for accelerating magnetic resonance imaging technique. However, Image reconstruction from subsampled data is an ill-posed problem. In the current study, the wavelet package has been applied to deep networks. The replacement of the conventional downsampling and upsampling layers with Discrete Wavelet Transform (DWT) and Inverse Wavelet Transform (IWT) improved the reconstruction results. Moreover, the consequence of this substitution has been investigated on potent densely connected deep networks. The proposed novelty resulted in promising performance improvement in MR Image reconstruction.
Original language | English |
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Title of host publication | 2022 45th International Conference on Telecommunications and Signal Processing, TSP 2022 |
Editors | Norbert Herencsar |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 212-215 |
Number of pages | 4 |
ISBN (Electronic) | 9781665469487 |
DOIs | |
Publication status | Published - 2022 |
Event | 45th International Conference on Telecommunications and Signal Processing, TSP 2022 - Virtual, Online, Czech Republic Duration: 13 Jul 2022 → 15 Jul 2022 |
Publication series
Name | 2022 45th International Conference on Telecommunications and Signal Processing, TSP 2022 |
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Conference
Conference | 45th International Conference on Telecommunications and Signal Processing, TSP 2022 |
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Country/Territory | Czech Republic |
City | Virtual, Online |
Period | 13/07/22 → 15/07/22 |
Bibliographical note
Publisher Copyright:© 2022 IEEE.
Keywords
- Deep learning
- Densely Connected Residual Network
- Magnetic resonance imaging
- MR Image Reconstruction