Interpretation of inhomogeneous magnetization transfer in myelin water using a four‐pool model with dipolar reservoirs
Bibliographic record
Abstract
Abstract Purpose To confirm ihMT's specificity to myelin, an ihMT presaturation module was combined with a Poon–Henkelman multi‐echo spin‐echo readout to separate the ihMT signal in myelin water from intra‐/extra‐cellular water. This study explored the relationship between two quantitative myelin imaging techniques and measured the ihMT signal of myelin water. Methods Six rats were injured; three were sacrificed three weeks post‐injury, and three were sacrificed eight weeks post‐injury, and three healthy control rats were also sacrificed. The nine formalin‐fixed rat spinal cords were imaged using a Poon–Henkelman multi‐echo spin‐echo readout with an ihMT prepulse at different strengths of filtering at 7T. Results The proposed model was able to characterize the ihMT decay signal in myelin water and intra‐/extra‐cellular water pool. From this proposed four‐pool model with dipolar order reservoirs, we see a drop in the fit parameter in the fasciculus gracilis white matter region of the three‐week post‐injury cord. and (non‐myelin) were estimated to be approx. 8 and 1.5 ms, respectively. Conclusion The drop in in the three‐week post‐injury cords suggests that could potentially distinguish between functional myelin and myelin debris; however, more studies are needed to confirm this.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".