Name
Abstract | ||
|---|---|---|
Gradient echo myelin water imaging (GRE-MWI) is an MRI technique to measure myelin concentration and involves the analysis of signal decay characteristics of the multi-echo gradient echo data. The method provides a myelin water fraction as a quantitative biomarker for myelin. In this work, a new sequence and post-processing methods were proposed to generate high quality GRE-MWI images at 3T and 7T. In order to capture the rapidly decaying myelin water signals, a bipolar readout GRE sequence was designed with "gradient pairing," compensating for the eddy current effects. The flip angle dependency from the multi-compartmental T1 effects was explored and avoided using a 2D multi-slice acquisition with a long TR. Additionally, the sequence was tested for the effects of inflow and magnetization transfer and demonstrated robustness to these error sources. Lastly, the temporal and spatial B0 inhomogeneity effects were mitigated by using the B0 navigator and field inhomogeneity corrections. Using the method, high-quality myelin water images were successfully generated for the in-vivo human brain at both field strengths. When the myelin water fraction at 3T and 7T were compared, they showed a good correlation (R2≥ 0.88; p < 0.001) with a larger myelin water fraction at 7T. The proposed method also opens the possibility of high resolution (isotropic 1.5 mm resolution) myelin water mapping at 7T. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1016/j.neuroimage.2018.11.040 | NeuroImage |
Keywords | Field | DocType |
Myelin,Myelin water imaging,T2* relaxation,Bipolar acquisition,Multi-echo gradient echo,Gradient pairing | Psychology,Flip angle,Cognitive psychology,Robustness (computer science),Pairing,Magnetization transfer,Eddy current,Nuclear magnetic resonance,Myelin | Journal |
Volume | ISSN | Citations |
188 | 1053-8119 | 1 |
PageRank | References | Authors |
0.36 | 6 | 10 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hyeong-Geol Shin | 1 | 8 | 2.16 |
| Se-Hong Oh | 2 | 6 | 2.12 |
| Masaki Fukunaga | 3 | 319 | 27.13 |
| Yoonho Nam | 4 | 25 | 3.70 |
| doohee lee | 5 | 1 | 1.04 |
| Woojin Jung | 6 | 27 | 3.33 |
| Minju Jo | 7 | 1 | 0.36 |
| Sooyeon Ji | 8 | 7 | 1.55 |
| Joon Yul Choi | 9 | 24 | 4.32 |
| Jongho Lee | 10 | 74 | 6.15 |