$T_{2}$-Selective Magnetization Preparation Pulses
Bibliographic record
Abstract
The purpose of this work was to present and evaluate a new method for directly designing T2-selective preparation pulses. Using a modified Shinnar-Le-Roux (SLR) transform, the design of T2-selective pulses becomes equivalent to designing a pair of polynomials one of which represents the longitudinal magnetization and the other the transverse magnetization. The polynomials enable one to directly analyze the various tradeoffs involved in the design. To evaluate the new method, a short-T2-selective magnetization preparation pulse was designed. Following the preparation pulse, a 2D Fourier transform (2DFT) multislice gradient echo sequence was used for imaging. For verification Bloch equation simulations were performed along with both in vivo and phantom scans. Phantom scans showed good signal suppression of long-T2 species. This is supported by good long-T2 signal suppression seen on the in vivo images. Simulations indicate that the pulse is robust to +/-150 Hz B0 inhomogeneities and +/-10% B1 inhomogeneities.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".