Name
Abstract | ||
|---|---|---|
•Recovering T2 distributions from MRI data is difficult, especially due to noise.•Noise robust recovery possible through machine learning on synthetic data.•Synthetic data is derived from biophysical models.•Validated on in-vivo/ex-vivo scans including a subject with multiple sclerosis. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1016/j.media.2020.101940 | Medical Image Analysis |
Keywords | DocType | Volume |
Machine learning,T2 relaxometry,Myelin water imaging | Journal | 69 |
ISSN | Citations | PageRank |
1361-8415 | 0 | 0.34 |
References | Authors | |
8 | 12 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Thomas Yu | 1 | 9 | 2.84 |
| Erick Jorge Canales-Rodríguez | 2 | 188 | 12.40 |
| Marco Pizzolato | 3 | 37 | 5.91 |
| Gian Franco Piredda | 4 | 0 | 1.01 |
| Tom Hilbert | 5 | 0 | 2.03 |
| Elda Fischi-Gomez | 6 | 0 | 0.34 |
| Matthias Weigel | 7 | 0 | 0.34 |
| Muhamed Barakovic | 8 | 11 | 2.18 |
| Meritxell Bach Cuadra | 9 | 326 | 23.59 |
| Cristina Granziera | 10 | 10 | 2.55 |
| Tobias Kober | 11 | 137 | 9.48 |
| Jean-Philippe Thiran | 12 | 2 | 2.77 |