Name
Abstract | ||
|---|---|---|
Compressed sensing magnetic resonance imaging (CS-MRI) is an active research topic in the field of inverse problems. Conventional CS-MRI algorithms usually exploit the sparse nature of MRI in an iterative manner. These optimization-based CS-MRI methods are often time-consuming at test time, and are based on fixed transform bases or shallow dictionaries, which limits modeling capacity. Recently, deep models have been introduced to the CS-MRI problem. One main challenge for CS-MRI methods based on deep learning is the trade-off between model performance and network size. We propose a recursive dilated network (RDN) for CS-MRI that achieves good performance while reducing the number of network parameters. We adopt dilated convolutions in each recursive block to aggregate multi-scale information within the MRI. We also adopt a modified shortcut strategy to help features flow into deeper layers. Experimental results show that the proposed RDN model achieves state-of-the-art performance in CS-MRI while using far fewer parameters than previously required. |
Year | Venue | Field |
|---|---|---|
2018 | THIRTY-SECOND AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE / THIRTIETH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE / EIGHTH AAAI SYMPOSIUM ON EDUCATIONAL ADVANCES IN ARTIFICIAL INTELLIGENCE | Computer science,Algorithm,Artificial intelligence,Machine learning,Compressed sensing,Recursion |
DocType | Citations | PageRank |
Conference | 2 | 0.36 |
References | Authors | |
13 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Liyan Sun | 1 | 11 | 2.22 |
| Zhiwen Fan | 2 | 29 | 3.15 |
| Yue Huang | 3 | 317 | 29.82 |
| Xinghao Ding | 4 | 591 | 52.95 |
| John Paisley | 5 | 1003 | 55.70 |