Name
Abstract | ||
|---|---|---|
•We have proposed a deep network for super-resolved tissue microstructure estimation.•The deep network integrates signal sparsity with super-resolution techniques.•We have also developed an approach for quantifying the estimation uncertainty.•Our method achieves more accurate high-resolution tissue microstructure estimation.•The uncertainty quantification results correlate with the estimation errors. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1016/j.media.2020.101885 | Medical Image Analysis |
Keywords | DocType | Volume |
Diffusion MRI,q-Space deep learning,Super-resolved tissue microstructure estimation,Uncertainty quantification | Journal | 67 |
ISSN | Citations | PageRank |
1361-8415 | 0 | 0.34 |
References | Authors | |
0 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yu Qin | 1 | 0 | 0.34 |
| Zhiwen Liu | 2 | 56 | 14.48 |
| Chenghao Liu | 3 | 0 | 1.69 |
| Yuxing Li | 4 | 12 | 2.89 |
| Xiangzhu Zeng | 5 | 13 | 4.24 |
| Chuyang Ye | 6 | 61 | 11.12 |