Time‐efficient fast spin echo imaging at 4.7 T with low refocusing angles
Bibliographic record
Abstract
An implementation of fast spin echo at 4.7 T designed for versatile and time-efficient T(2)-weighted imaging of the human brain is presented. Reduced refocusing angles (alpha < 180 degrees) were employed to overcome specific absorption rate (SAR) constraints and their effects on image quality assessed. Image intensity and tissue contrast variations from heterogeneous RF transmit fields and incidental magnetization transfer effects were investigated at reduced refocusing angles. We found that intraslice signal variations are minimized with refocusing angles near 180 degrees, but apparent gray/white matter contrast is independent of refocusing angle. Incidental magnetization transfer effects from multislice acquisitions were shown to attenuate white matter intensity by 25% and gray matter intensity by 15% at 180 degrees; less than 5% attenuation was seen in all tissues at flip angles below 60 degrees. We present multislice images acquired without excess delay time for SAR mitigation using a variety of protocols. Subsecond half Fourier acquisition single-shot turbo spin echo (HASTE) images were obtained with a novel variable refocusing angle echo train (20 degrees < alpha < 58 degrees) and high-resolution scans with a voxel volume of 0.18 mm(3) were acquired in 6.5 min with refocusing angles of 100 degrees.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".