Name
Title | ||
|---|---|---|
A Deep Information Sharing Network for Multi-contrast Compressed Sensing MRI Reconstruction. |
Abstract | ||
|---|---|---|
Compressed sensing (CS) theory can accelerate multi-contrast magnetic resonance imaging (MRI) by sampling fewer measurements within each contrast. However, conventional optimization-based reconstruction models suffer several limitations, including a strict assumption of shared sparse support, time-consuming optimization, and “shallow” models with difficulties in encoding the patterns contained in ... |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/TIP.2019.2925288 | IEEE Transactions on Image Processing |
Keywords | Field | DocType |
Transmitters,Receivers,Fading channels,Network coding,Output feedback,Numerical models,Nickel | Fidelity,Pattern recognition,Inference,Computer science,Robustness (computer science),Sampling (statistics),Artificial intelligence,Deep learning,Information sharing,Compressed sensing,Encoding (memory) | Journal |
Volume | Issue | ISSN |
28 | 12 | 1057-7149 |
Citations | PageRank | References |
7 | 0.44 | 9 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Liyan Sun | 1 | 14 | 3.98 |
| Zhiwen Fan | 2 | 29 | 3.15 |
| Yue Huang | 3 | 317 | 29.82 |
| Xinghao Ding | 4 | 591 | 52.95 |
| John Paisley | 5 | 1003 | 55.70 |