Flip angle mapping with the accelerated 3D look‐locker sequence
Bibliographic record
Abstract
PURPOSE: A new approach to mapping the flip angle quickly and efficiently in 3D based on the Look-Locker technique is presented. METHODS: We modified the accelerated 3D Look-Locker T1 measurement technique to allow rapid measurement of flip angle. By removing the inversion pulses and interleaving two radio frequency pulses with different amplitude, it is possible to fit directly for the true flip angle using a reduced number of parameters. This technique, non-inverted Double Angle Look-Locker, allows quick and efficient mapping of the flip angle in 3D. RESULTS: non-inverted Double Angle Look-Locker is validated in vitro against the actual flip angle imaging technique for a range of flip angles and T1 values. Flip angle maps produced with non-inverted Double Angle Look-Locker can be acquired in approximately 1 min, and are accurate to within 10% of the actual flip angle imaging measurement. It is shown to accurately measure the excited slab profile of several different pulses. An application to correcting in vivo DESPOT T1 data is presented. CONCLUSION: The presented technique is a rapid method for mapping flip angles across a 3D volume, capable of producing a flip angle map in approximately 1 min.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".