Name
Title | ||
|---|---|---|
A Kernel-based Low-rank (KLR) Model for Low-dimensional Manifold Recovery in Highly Accelerated Dynamic MRI. |
Abstract | ||
|---|---|---|
While many low rank and sparsity-based approaches have been developed for accelerated dynamic magnetic resonance imaging (dMRI), they all use low rankness or sparsity in input space, overlooking the intrinsic nonlinear correlation in most dMRI data. In this paper, we propose a kernel-based framework to allow nonlinear manifold models in reconstruction from sub-Nyquist data. Within this framework, ... |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/TMI.2017.2723871 | IEEE Transactions on Medical Imaging |
Keywords | Field | DocType |
Manifolds,Kernel,Image reconstruction,Principal component analysis,Magnetic resonance imaging,Data models | Kernel (linear algebra),Data modeling,Mathematical optimization,Feature vector,Pattern recognition,Radial basis function kernel,Kernel embedding of distributions,Kernel principal component analysis,Polynomial kernel,Artificial intelligence,Nonlinear dimensionality reduction,Mathematics | Journal |
Volume | Issue | ISSN |
36 | 11 | 0278-0062 |
Citations | PageRank | References |
7 | 0.49 | 27 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nakarmi, U. | 1 | 31 | 5.14 |
| Yanhua Wang | 2 | 47 | 6.35 |
| Jingyuan Lyu | 3 | 17 | 2.83 |
| Dong Liang | 4 | 131 | 14.36 |
| Leslie Ying | 5 | 240 | 29.08 |