Name
Abstract | ||
|---|---|---|
A new algorithm is proposed for compressed sensingmagnetic resonance imaging (CS-MRI). The lp-norm (0 <; p ≤ 1) based adaptive regularization model is used for MRI. The algorithm is established by using a novel iterative shrinkage scheme. In the iteration, the quasi-Newton method is employed. In the shrinkage, the threshold is defined varyingly. Also, the parameter p is selected dynamically in the... |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/LSP.2017.2736159 | IEEE Signal Processing Letters |
Keywords | Field | DocType |
Compressed sensing,Image reconstruction,Magnetic resonance imaging | Iterative reconstruction,Mathematical optimization,Quasi-Newton method,Pattern recognition,Shrinkage,Algorithm,Artificial intelligence,Adaptive regularization,Mathematics | Journal |
Volume | Issue | ISSN |
24 | 10 | 1070-9908 |
Citations | PageRank | References |
4 | 0.40 | 12 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chen Zhen | 1 | 31 | 15.05 |
| Yuli Fu | 2 | 200 | 29.90 |
| Youjun Xiang | 3 | 4 | 2.09 |
| Rong Rong | 4 | 12 | 3.66 |