Multi‐gradient‐echo myelin water fraction imaging: Comparison to the multi‐echo‐spin‐echo technique
Bibliographic record
Abstract
Purpose Myelin water fraction (MWF) mapping based on multi‐gradient recalled‐echo (MGRE) imaging has been proposed as an alternative to the conventional multi‐echo‐spin‐echo (MESE) approach. In this work, we performed a comparative study of MESE and MGRE‐derived MWFs in the same subject group. Methods MESE and MGRE data were acquired in 12 healthy volunteers at 3T. decay curves were corrected for the effects of field inhomogeneities and multicomponent analysis of and signals was performed using non‐negative least‐squares fitting. Results When comparing MGRE and MESE‐MWFs across volunteers, no significant differences were observed between average values in WM, deep GM (dGM), and cortical GM (cGM) that were (14 ± 3%), (6 ± 2%), and (8 ± 2%) for MGRE, and (13 ± 2%), (6 ± 1%), and (7 ± 1%), respectively, for MESE. The MGRE and MESE‐MWFs showed a strong correlation (r 2 = 0.84) and Bland‐Altman analysis revealed a small positive bias of (0.8 ± 1.6%) (absolute difference) for the MGRE‐MWF. Conclusion Overall, we observed excellent agreement between the two techniques. The small positive bias of the MGRE‐MWF is thought to be a consequence of its potentially reduced sensitivity to water exchange effects, compared to the MESE‐MWF. This work suggests that with careful correction for the effects of field inhomogeneities, MGRE‐MWF imaging is a promising alternative to the MESE approach. Magn Reson Med 79:1439–1446, 2018. © 2017 International Society for Magnetic Resonance in Medicine.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".