Name
Abstract | ||
|---|---|---|
Dynamic magnetic resonance imaging (MRI) exhibits high correlations in k-space and time. In order to accelerate the dynamic MR imaging and to exploit k-t correlations from highly undersampled data, here we propose a novel deep learning based approach for dynamic MR image reconstruction, termed k-t NEXT (k-t NEtwork with X-f Transform). In particular, inspired by traditional methods such as k-t BLAST and k-t FOCUSS, we propose to reconstruct the true signals from aliased signals in x-f domain to exploit the spatio-temporal redundancies. Building on that, the proposed method then learns to recover the signals by alternating the reconstruction process between the x-f space and image space in an iterative fashion. This enables the network to effectively capture useful information and jointly exploit spatio-temporal correlations from both complementary domains. Experiments conducted on highly undersampled short-axis cardiac cine MRI scans demonstrate that our proposed method outperforms the current state-of-the-art dynamic MR reconstruction approaches both quantitatively and qualitatively. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1007/978-3-030-32245-8_56 | Lecture Notes in Computer Science |
DocType | Volume | ISSN |
Conference | 11765 | 0302-9743 |
Citations | PageRank | References |
1 | 0.36 | 0 |
Authors | ||
7 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chen Qin | 1 | 79 | 11.70 |
| Jo Schlemper | 2 | 180 | 8.11 |
| Jinming Duan | 3 | 130 | 19.92 |
| Gavin Seegoolam | 4 | 1 | 0.36 |
| Anthony N Price | 5 | 253 | 15.32 |
| Jo Hajnal | 6 | 1796 | 119.03 |
| Daniel Rueckert | 7 | 9338 | 637.58 |