Bandwidth‐modulated adiabatic RF pulses for uniform selective saturation and inversion
Bibliographic record
Abstract
Radiofrequency (RF) inversion and saturation pulses with extremely high spatial selectivity and uniform profiles are a requirement for numerous MR techniques, such as pulsed arterial spin labeling and outer volume suppression. Adiabatic pulses used for inversion of longitudinal magnetization are ubiquitous, but the superior selectivity of adiabatic full passages has not been widely exploited for saturation because a simple way of calibrating the amplitude of these subadiabatic pulses is lacking. An analytically derived calibration equation is presented, applicable to a large class of pulses including the hyperbolic secant (HS) pulse and allowing the determination of the precise amplitude required to achieve any effective flip angle. The properties of this calibration are examined, and a highly selective and homogeneous HS saturation pulse is demonstrated. Based on this calibration a new class of RF pulses is developed. These bandwidth-modulated adiabatic selective saturation and inversion (BASSI) RF pulses afford optimal amplitude modulation, achieving uniform profiles at any effective flip angle. BASSI pulses are compared to existing gradient modulated adiabatic pulses in simulations and phantom experiments and shown to be superior in terms of selectivity and homogeneity, while requiring less RF energy. An application of BASSI pulses to pulsed arterial spin labeling is shown.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".