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 (MTsat) maps can be corrected with use of an R1 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 R1 (R1,obs) and apparent bound pool size ( ) in the brain. MTsat values were simulated for a range of , R1,obs, and . An equation was fit to the simulated MTsat, then a linear relationship between R1,obs and was generated. These results were used to generate correction factor maps for the MTsat 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 R1,obs (r > 0.96), permitting the use of R1,obs to estimate for correction. All corrected MTsat maps displayed a decreased correlation with compared to uncorrected MTsat and MTsat 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) MTsat maps. Conclusion The proposed correction decreases the dependence of MTsat 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 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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".