Name
Abstract | ||
|---|---|---|
To improve the compressive sensing MRI (CS-MRI) approaches in terms of fine structure loss under high acceleration factors, we have proposed an iterative feature refinement model (IFR-CS), equipped with fixed transforms, to restore the meaningful structures and details. Nevertheless, the proposed IFR-CS still has some limitations, such as the selection of hyper-parameters, a lengthy reconstruction time, and the fixed sparsifying transform. To alleviate these issues, we unroll the iterative feature refinement procedures in IFR-CS to a supervised model-driven network, dubbed IFR-Net. Equipped with training data pairs, both regularization parameter and the utmost feature refinement operator in IFR-CS become trainable. Additionally, inspired by the powerful representation capability of convolutional neural network (CNN), CNN-based inversion blocks are explored in the sparsity-promoting denoising module to generalize the sparsity-enforcing operator. Extensive experiments on both simulated and
<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in vivo</italic>
MR datasets have shown that the proposed network possesses a strong capability to capture image details and preserve well the structural information with fast reconstruction speed. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/TCI.2019.2956877 | IEEE Transactions on Computational Imaging |
Keywords | Field | DocType |
Transforms,Image reconstruction,Iterative methods,Image restoration,Biomedical imaging,Noise reduction | Computer vision,Artificial intelligence,Compressed sensing,Mathematics | Journal |
Volume | ISSN | Citations |
6 | 2573-0436 | 6 |
PageRank | References | Authors |
0.41 | 0 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yiling Liu | 1 | 9 | 2.13 |
| Qiegen Liu | 2 | 249 | 28.53 |
| Minghui Zhang | 3 | 7 | 0.75 |
| Qingxin Yang | 4 | 6 | 0.41 |
| Shanshan Wang | 5 | 27 | 9.31 |
| Dong Liang | 6 | 131 | 14.36 |