Name
Abstract | ||
|---|---|---|
Compressed sensing based parallel imaging (CS-PI) has attracted great attention in fast magnetic resonance imaging (MRI) community. In particular, Bregman iterative model has shown encouraging performance in solving this problem. However, its regularization term still has large room for improvement. In this work, we propose a network-driven prior induced Bregman model, dubbed as Breg-EDAEP, for CS-PI task. In the present model, the implicit property among different channel MR images is preliminarily explored by the network to obtain more structure details in iterative reconstruction procedure. Experiments on various acceleration factors and sampling patterns have shown that the proposed method outperforms the state-of-the-art algorithms. Breg-EDAEP possesses strong capability to restore image details and preserves well structure information. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/EMBC.2019.8856914 | 2019 41ST ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC) |
Keywords | Field | DocType |
Compressed Sensing, Parallel Imaging, Bregman Iteration, Deep Learning, Network-driven Prior | Iterative reconstruction,Computer vision,Task analysis,Iterative and incremental development,Computer science,Algorithm,Communication channel,Regularization (mathematics),Artificial intelligence,Sampling (statistics),Acceleration,Compressed sensing | Conference |
Volume | ISSN | Citations |
2019 | 1557-170X | 0 |
PageRank | References | Authors |
0.34 | 0 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Guan-Yu Li | 1 | 2 | 4.42 |
| Yiling Liu | 2 | 9 | 2.13 |
| Minghui Zhang | 3 | 1 | 1.37 |
| Shanshan Wang | 4 | 133 | 6.48 |
| Yanjie Zhu | 5 | 0 | 0.34 |
| Qiegen Liu | 6 | 249 | 28.53 |
| Dong Liang | 7 | 47 | 10.50 |