Name
Abstract | ||
|---|---|---|
Accelerating the data acquisition of dynamic magnetic resonance imaging leads to a challenging ill-posed inverse problem, which has received great interest from both the signal processing and machine learning communities over the last decades. The key ingredient to the problem is how to exploit the temporal correlations of the MR sequence to resolve aliasing artifacts. Traditionally, such observat... |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/TMI.2018.2863670 | IEEE Transactions on Medical Imaging |
Keywords | DocType | Volume |
Image reconstruction,Magnetic resonance imaging,Optimization,Machine learning,Iterative methods,Recurrent neural networks | Journal | 38 |
Issue | ISSN | Citations |
1 | 0278-0062 | 25 |
PageRank | References | Authors |
0.66 | 17 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chen Qin | 1 | 79 | 11.70 |
| Jo Schlemper | 2 | 180 | 8.11 |
| Jose Caballero | 3 | 663 | 22.59 |
| Anthony N Price | 4 | 253 | 15.32 |
| Jo Hajnal | 5 | 1796 | 119.03 |
| Daniel Rueckert | 6 | 9338 | 637.58 |