Name
Abstract | ||
|---|---|---|
Recent theoretical results on low-rank matrix reconstruction have inspired significant interest in low-rank modeling of MRI images. Existing approaches have focused on higher-dimensional scenarios with data available from multiple channels, timepoints, or image contrasts. The present work demonstrates that single-channel, single-contrast, single-timepoint k-space data can also be mapped to low-ran... |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/TMI.2013.2293974 | IEEE Transactions on Medical Imaging |
Keywords | Field | DocType |
Approximation methods,Fourier transforms,Image reconstruction,Magnetic resonance imaging,Matrix decomposition | Matrix (mathematics),Minimisation (psychology),Minification,Regularization (mathematics),Artificial intelligence,Iterative reconstruction,Computer vision,Mathematical optimization,k-space,Pattern recognition,Matrix decomposition,Feature extraction,Mathematics | Journal |
Volume | Issue | ISSN |
33 | 3 | 0278-0062 |
Citations | PageRank | References |
14 | 0.79 | 0 |
Authors | ||
1 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Justin P. Haldar | 1 | 350 | 35.40 |