Field inhomogeneity correction for gradient echo myelin water fraction imaging
Bibliographic record
Abstract
Purpose Recently, the multi‐echo gradient echo (MGRE) sequence has been proposed for multicomponent (MC ) based myelin water fraction (MWF) mapping. This approach has appeal because it can provide fast whole‐brain coverage, has low specific absorption rate, and short echo spacing. However, the MGRE signal requires correction for accurate MWF mapping, because of its sensitivity to magnetic field inhomogeneities (ΔB0). We propose a ΔB0 correction method for 2D MGRE data obtained for MWF mapping. Theory and Methods Latter‐echo MGRE data were fit to estimate B0 gradients in the slice‐select direction ( ). The decay signal was corrected for the effects of , and MC analysis was performed using nonnegative least‐squares fitting. The method was evaluated using simulations and its performance demonstrated in healthy volunteers. Results Simulations showed that MWFs are significantly biased in the presence of and that our correction method leads to accurate MWF estimates. In vivo MWF maps obtained from corrected data showed recovery of MWF estimates in areas of high ΔB0, and overall good agreement with literature values obtained with the reference MC ‐based method. Conclusion A new algorithm was presented for ΔB0 correction of 2D MGRE echo data acquired for MWF imaging. Simulations and in vivo data showed an improvement in MWF estimates. Magn Reson Med 78:49–57, 2017. © 2016 International Society for Magnetic Resonance in Medicine
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".