Name
Title | ||
|---|---|---|
Denoising of 3-D Magnetic Resonance Images Using a Residual Encoder-Decoder Wasserstein Generative Adversarial Network. |
Abstract | ||
|---|---|---|
•A MRI denoising method based on the WGAN framework is proposed.•The ideas of residual network and autoencoder are imposed to maintain the structural details and edges, which are clinically important.•With a proper training procedure, our method yields competitive results with several state-of-art methods.•The generalization and robustness of our proposed model were carefully sensed by training and testing with different data sources, including simulated and real noise.•Our method is highly computationally fast, and well compatible for parallel implementation on graphic processing units (GPUs). |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1016/j.media.2019.05.001 | Medical Image Analysis |
Keywords | Field | DocType |
Magnetic resonance imaging (MRI),Image denoising,Deep learning,Wasserstein GAN,Perceptual loss | Noise reduction,Residual,Feature vector,Generative adversarial network,Pattern recognition,Deconvolution,Mean squared error,Artificial intelligence,Deep learning,Mathematics,Magnetic resonance imaging | Journal |
Volume | ISSN | Citations |
55 | 1361-8415 | 4 |
PageRank | References | Authors |
0.42 | 37 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Maosong Ran | 1 | 5 | 0.76 |
| Jinrong Hu | 2 | 6 | 3.16 |
| Yang Chen | 3 | 209 | 29.24 |
| Hu Chen | 4 | 73 | 5.73 |
| Huaiqiang Sun | 5 | 23 | 2.09 |
| Jiliu Zhou | 6 | 450 | 58.21 |
| Yi Zhang | 7 | 118 | 7.86 |