Name
Title | ||
|---|---|---|
DIMENSION: Dynamic MR imaging with both k-space and spatial prior knowledge obtained via multi-supervised network training |
Abstract | ||
|---|---|---|
Dynamic MR image reconstruction from incomplete k-space data has generated great research interest due to its capability in reducing scan time. Nevertheless, the reconstruction problem is still challenging due to its ill-posed nature. Most existing methods either suffer from long iterative reconstruction time or explore limited prior knowledge. This paper proposes a dynamic MR imaging method with both k-space and spatial prior knowledge integrated via multi-supervised network training, dubbed as DIMENSION. Specifically, the DIMENSION architecture consists of a frequential prior network for updating the k-space with its network prediction and a spatial prior network for capturing image structures and details. Furthermore, a multi-supervised network training technique is developed to constrain the frequency domain information and the spatial domain information. The comparisons with classical k-t FOCUSS, k-t SLR, L+S and the state-of-the-art CNN-based method on in vivo datasets show our method can achieve improved reconstruction results in shorter time. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1002/nbm.4131 | NMR IN BIOMEDICINE |
Keywords | Field | DocType |
compressed sensing, deep learning, dynamic MR imaging, k-space prior, multi-supervised | Mr imaging,Frequency domain,Iterative reconstruction,k-space,Reconstruction problem,Pattern recognition,Computer science,Scan time,Artificial intelligence | Journal |
Volume | Issue | ISSN |
35 | 4 | 0952-3480 |
Citations | PageRank | References |
3 | 0.36 | 0 |
Authors | ||
7 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shanshan Wang | 1 | 27 | 9.31 |
| Ziwen Ke | 2 | 14 | 1.87 |
| Huitao Cheng | 3 | 3 | 0.36 |
| Sen Jia | 4 | 6 | 2.80 |
| Leslie Ying | 5 | 240 | 29.08 |
| Hairong Zheng | 6 | 56 | 28.24 |
| Dong Liang | 7 | 55 | 5.66 |