Name
Title | ||
|---|---|---|
Smooth robust tensor principal component analysis for compressed sensing of dynamic MRI. |
Abstract | ||
|---|---|---|
•The unsupervised reconstruction methods for dynamic MRI are briefly summarized.•A smooth robust tensor principle component analysis (SRTPCA) method is proposed for dynamic MRI reconstruction.•Numerical experiments on cardiac perfusion and cine datasets show the proposed SRTPCA method outperforms the state-of-the-art ones. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1016/j.patcog.2020.107252 | Pattern Recognition |
Keywords | Field | DocType |
Robust tensor principal component analysis,Compressed sensing,Low rank tensor approximation,Tensor total variation,Dynamic magnetic resonance imaging | Pattern recognition,Tensor,Sparse approximation,Robust principal component analysis,Total variation denoising,Artificial intelligence,Smoothness,Convex optimization,Principal component analysis,Mathematics,Compressed sensing | Journal |
Volume | Issue | ISSN |
102 | 1 | 0031-3203 |
Citations | PageRank | References |
4 | 0.44 | 30 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yipeng Liu | 1 | 43 | 5.93 |
| Tengteng Liu | 2 | 4 | 0.44 |
| Jiani Liu | 3 | 9 | 1.18 |
| Ce Zhu | 4 | 1473 | 117.79 |