Name
Abstract | ||
|---|---|---|
We look into robustness of deep learning based MRI reconstruction when tested on unseen contrasts and organs. We then propose to generalise the network by training with large publicly-available natural image datasets with synthesised phase information to achieve high cross-domain reconstruction performance which is competitive with domain-specific training. To explain its generalisation mechanism, we have also analysed patch sets for different training datasets. |
Year | Venue | DocType |
|---|---|---|
2019 | arXiv: Computer Vision and Pattern Recognition | Journal |
Volume | Citations | PageRank |
abs/1902.10815 | 0 | 0.34 |
References | Authors | |
4 | 8 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Cheng Ouyang | 1 | 0 | 1.01 |
| Jo Schlemper | 2 | 26 | 1.80 |
| Carlo Biffi | 3 | 27 | 4.97 |
| Gavin Seegoolam | 4 | 0 | 0.34 |
| Jose Caballero | 5 | 663 | 22.59 |
| Anthony N Price | 6 | 253 | 15.32 |
| Jo Hajnal | 7 | 1796 | 119.03 |
| Daniel Rueckert | 8 | 9338 | 637.58 |