A model‐based framework for correcting inhomogeneity effects in magnetization transfer saturation and inhomogeneous magnetization transfer saturation maps
Bibliographic record
Abstract
Purpose In this work, we propose that Δ ‐induced errors in magnetization transfer (MT) saturation (MT sat ) maps can be corrected with use of an R 1 and map and through numerical simulations of the sequence. Theory and Methods One healthy subject was scanned at 3.0T using a partial quantitative MT protocol to estimate the relationship between observed R 1 (R 1,obs ) and apparent bound pool size ( ) in the brain. MT sat values were simulated for a range of , R 1,obs , and . An equation was fit to the simulated MT sat , then a linear relationship between R 1,obs and was generated. These results were used to generate correction factor maps for the MT sat acquired from single‐point data. The proposed correction was compared to an empirical correction factor with different MT‐preparation schemes. Results was highly correlated with R 1,obs (r > 0.96), permitting the use of R 1,obs to estimate for correction. All corrected MT sat maps displayed a decreased correlation with compared to uncorrected MT sat and MT sat corrected with an empirical factor in the corpus callosum. There was good agreement between the proposed approach and the empirical correction with radiofrequency saturation at 2 kHz, with larger deviations seen when using saturation pulses further off‐resonance and in inhomogeneous (ih) MT sat maps. Conclusion The proposed correction decreases the dependence of MT sat on inhomogeneities. Furthermore, this flexible framework permits the use of different saturation protocols, making it useful for correcting inhomogeneities in ihMT.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".