Silent T<sub>1</sub> mapping using the variable flip angle method with B<sub>1</sub> correction
Bibliographic record
Abstract
Purpose To compare the silent rotating ultrafast imaging sequence (RUFIS) to a traditional Cartesian spoiled gradient‐echo (SPGR) acquisition scheme for variable flip angle (VFA) mapping. Methods A two‐point VFA measurement was performed using RUFIS and Cartesian SPGR in a quantitative phantom and healthy volunteers. To correct for errors, a novel silent magnetization prepared map acquisition (SIMBA) was developed, which combined with RUFIS VFA allows for a completely silent mapping protocol. Results The silent protocol was found to have comparable repeatability but higher reproducibility in vivo compared to the standard SPGR protocol, and showed no increase in acoustic noise levels above background noise levels compared to a 33 dBA increase for the SPGR acquisition. Conclusions VFA mapping using RUFIS is a feasible alternative to SPGR, achieving silent mapping with comparable acquisition time.
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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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 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".