A technique for rapid single‐echo spin‐echo <i>T</i><sub>2</sub> mapping
Bibliographic record
Abstract
A rapid technique for mapping of T(2) relaxation times is presented. The method is based on the conventional single-echo spin echo approach but uses a much shorter pulse repetition time to accelerate data acquisition. The premise of the new method is the use of a constant difference between the echo time and pulse repetition time, which removes the conventional and restrictive requirement of pulse repetition time >> T(1). Theoretical and simulation investigations were performed to evaluate the criteria for accurate T(2) measurements. Measured T(2)s were shown to be within 1% error as long as the key criterion of pulse repetition time/T(2) > or =3 is met. Strictly, a second condition of echo time/T(1) << 1 is also required. However, violations of this condition were found to have minimal impact in most clinical scenarios. Validation was conducted in phantoms and in vivo T(2) mapping of healthy cartilage and brain. The proposed method offers all the advantages of single-echo spin echo imaging (e.g., immunity to stimulated echo effects, robustness to static field inhomogeneity, flexibility in the number and choice of echo times) in a considerably reduced amount of time and is readily implemented on any clinical scanner.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".