Name
Title | ||
|---|---|---|
Unsupervised resolution-agnostic quantitative susceptibility mapping using adaptive instance normalization |
Abstract | ||
|---|---|---|
•Unsupervised deep learning with physical model for quantitative susceptibility mapping.•Adaptive instance normalization allows resolution-agnostic reconstruction.•The proposed method is generalizable to various resolution data without streaking artifacts. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1016/j.media.2022.102477 | Medical Image Analysis |
Keywords | DocType | Volume |
Quantitative susceptibility mapping,Unsupervised deep learning,Adaptive instance normalization,Resolution-agnostic | Journal | 79 |
ISSN | Citations | PageRank |
1361-8415 | 0 | 0.34 |
References | Authors | |
0 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Gyutaek Oh | 1 | 0 | 0.68 |
| Hyokyoung Bae | 2 | 0 | 0.34 |
| Hyun-Seo Ahn | 3 | 0 | 0.34 |
| Sung-Hong Park | 4 | 0 | 0.34 |
| Won-Jin Moon | 5 | 5 | 2.49 |
| Jong Chul Ye | 6 | 715 | 79.99 |