How does magnetization transfer influence mc<scp>DESPOT</scp> results?
Bibliographic record
Abstract
Purpose A steady‐state approach that was termed multicomponent driven equilibrium single pulse observation of T1 and T2 (mcDESPOT) has recently been proposed for myelin water fraction (fM) mapping in brain development and demyelinating diseases. However, fMs estimated by mcDESPOT are significantly higher than myelin water fraction derived from multiecho spin echo T2‐decay curve approaches. Magnetization transfer (MT), enhanced by the use of short, relatively high amplitude radiofrequency (RF) pulses in mcDESPOT, may artifactually influence fM maps. Our goal was to investigate the role of MT in mcDESPOT. Methods mcDESPOT data was collected twice from three healthy volunteers using short RF pulses with higher MT effect and long RF pulses with lower MT effect. MR parameters from 11 white and gray regions were compared using a paired student t‐test. Whole slice difference images were also compared. Results MT effects had a substantial influence on the signal generated by the balanced steady‐state free procession sequences used in mcDESPOT. However, these MT effects were not clearly evident in the fM values determined by the conventional two‐pool mcDESPOT analysis. Conclusion The signal generated from mcDESPOT is sensitive to MT, but the extracted myelin water fractions are relatively insensitive to changes of MT. Magn Reson Med 74:1327–1335, 2015. © 2014 Wiley Periodicals, Inc.
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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 | 0.012 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".