Name
Abstract | ||
|---|---|---|
In the area of magnetic resonance imaging (MRI), an extensive range of non-linear reconstruction algorithms has been proposed which can be used with general Fourier subsampling patterns. However, the design of these subsampling patterns has typically been considered in isolation from the reconstruction rule and the anatomy under consideration. In this paper, we propose a learning-based framework f... |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/TMI.2018.2832540 | IEEE Transactions on Medical Imaging |
Keywords | DocType | Volume |
Magnetic resonance imaging,Decoding,Image reconstruction,Training,Machine learning,Reconstruction algorithms,Compressed sensing | Journal | 37 |
Issue | ISSN | Citations |
6 | 0278-0062 | 5 |
PageRank | References | Authors |
0.42 | 0 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Baran Gozcu | 1 | 12 | 1.57 |
| Rabeeh Karimi Mahabadi | 2 | 6 | 2.45 |
| Yen-Huan Li | 3 | 12 | 2.89 |
| Efe Ilicak | 4 | 7 | 0.79 |
| Tolga Çukur | 5 | 36 | 8.84 |
| Jonathan Scarlett | 6 | 163 | 31.49 |
| Volkan Cevher | 7 | 6 | 1.77 |