Name
Abstract | ||
|---|---|---|
Compressed sensing (CS) has been introduced to accelerate data acquisition in MR Imaging. However, CS-MRI methods suffer from detail loss with large acceleration and complicated parameter selection. To address the limitations of existing CS-MRI methods, a model-driven MR reconstruction is proposed that trains a deep network, named CP-net, which is derived from the Chambolle-Pock algorithm to reconstruct the in vivo MR images of human brains from highly undersampled complex k-space data acquired on different types of MR scanners. The proposed deep network can learn the proximal operator and parameters among the Chambolle-Pock algorithm. All of the experiments show that the proposed CP-net achieves more accurate MR reconstruction results, outperforming state-of-the-art methods across various quantitative metrics. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/EMBC.2019.8857141 | 2019 41ST ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC) |
Field | DocType | Volume |
Mr imaging,Computer vision,Computer science,Data acquisition,Acceleration,Artificial intelligence,Operator (computer programming),Compressed sensing | Conference | 2019 |
ISSN | Citations | PageRank |
1557-170X | 0 | 0.34 |
References | Authors | |
0 | 11 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Haifeng Wang | 1 | 21 | 5.87 |
| Jing Cheng | 2 | 50 | 14.53 |
| Sen Jia | 3 | 6 | 2.80 |
| Zhilang Qiu | 4 | 1 | 1.72 |
| Caiyun Shi | 5 | 0 | 1.35 |
| Lixian Zou | 6 | 0 | 1.01 |
| Shi Su | 7 | 0 | 2.70 |
| Yuchou Chang | 8 | 194 | 15.86 |
| Yanjie Zhu | 9 | 0 | 0.68 |
| Leslie Ying | 10 | 240 | 29.08 |
| Dong Liang | 11 | 131 | 14.36 |