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- Yoo, Jaejun
- Lab. of Advanced Imaging Technology
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| DC Field | Value | Language |
|---|---|---|
| dc.citation.conferencePlace | US | - |
| dc.citation.title | ISMRM 25th Annual Meeting & Exhibition | - |
| dc.contributor.author | Lee, Dongwook | - |
| dc.contributor.author | Yoo, Jaejun | - |
| dc.contributor.author | Ye, Jong Chul | - |
| dc.date.accessioned | 2023-12-19T19:08:15Z | - |
| dc.date.available | 2023-12-19T19:08:15Z | - |
| dc.date.created | 2021-08-19 | - |
| dc.date.issued | 2017-04-25 | - |
| dc.description.abstract | A deep residual learning algorithm is proposed to reconstruct MR images from highly down-sampled k-space data. After formulating a compressed sensing problem as a residual regression problem, a deep convolutional neural network (CNN) was designed to learn the aliasing artifacts. The residual learning algorithm took only 30-40ms with significantly better reconstruction performance compared to GRAPPA and the state-of-the-art compressed sensing algorithm, ALOHA. | - |
| dc.identifier.bibliographicCitation | ISMRM 25th Annual Meeting & Exhibition | - |
| dc.identifier.uri | https://scholarworks.unist.ac.kr/handle/201301/53624 | - |
| dc.publisher | International Society for Magnetic Resonance in Medicine | - |
| dc.title | Compressed Sensing and Parallel MRI using Deep Residual Learning | - |
| dc.type | Conference Paper | - |
| dc.date.conferenceDate | 2017-04-22 | - |
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