Name
Abstract | ||
|---|---|---|
Due to the high cost and low accessibility of 7T magnetic resonance imaging (MRI) scanners, we propose a novel dual-domain cascaded regression framework to synthesize 7T images from the routine 3T images. Our framework is composed of two parallel and interactive multi-stage regression streams, where one stream regresses on spatial domain and the other regresses on frequency domain. These two streams complement each other and enable the learning of complex mappings between 3T and 7T images. We evaluated the proposed framework on a set of 3T and 7T images by leave-one-out cross-validation. Experimental results demonstrate that the proposed framework generates realistic 7T images and achieves better results than state-of-the-art methods. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1007/978-3-030-00928-1_47 | Lecture Notes in Computer Science |
Field | DocType | Volume |
Frequency domain,Computer vision,Regression,Pattern recognition,Computer science,Artificial intelligence,Magnetic resonance imaging | Conference | 11070 |
ISSN | Citations | PageRank |
0302-9743 | 0 | 0.34 |
References | Authors | |
6 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yongqin Zhang | 1 | 83 | 6.54 |
| Jie-Zhi Cheng | 2 | 102 | 13.00 |
| Lei Xiang | 3 | 172 | 12.47 |
| Pew-Thian Yap | 4 | 1093 | 93.77 |
| Dinggang Shen | 5 | 7837 | 611.27 |