Name
Abstract | ||
|---|---|---|
High-dimensional MR imaging often requires long data acquisition time, thereby limiting its practical applications. This paper presents a low-rank tensor based method for accelerated high-dimensional MR imaging using sparse sampling. This method represents high-dimensional images as low-rank tensors (or partially separable functions) and uses this mathematical structure for sparse sampling of the ... |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/TMI.2016.2550204 | IEEE Transactions on Medical Imaging |
Keywords | Field | DocType |
Tensile stress,Image reconstruction,Acceleration,Data acquisition,Biomedical imaging,Nickel | Mr imaging,Iterative reconstruction,Mathematical optimization,Subspace topology,Tensor,Mathematical structure,Data acquisition,Separable space,Sampling (statistics),Mathematics | Journal |
Volume | Issue | ISSN |
35 | 9 | 0278-0062 |
Citations | PageRank | References |
7 | 0.46 | 0 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jingfei He | 1 | 11 | 1.24 |
| Qiegen Liu | 2 | 249 | 28.53 |
| Anthony G. Christodoulou | 3 | 44 | 6.33 |
| Chao Ma | 4 | 9 | 1.89 |
| Fan Lam | 5 | 50 | 9.14 |
| Zhi-Pei Liang | 6 | 522 | 64.94 |