Reducing contamination while closing the gap: BASSI RF pulses in PASL
Bibliographic record
Abstract
Bandwidth-modulated selective saturation and inversion (BASSI) pulses are a class of frequency- and gradient-modulated radiofrequency (RF) pulses, derived from the hyperbolic secant pulse by temporal variation of the bandwidth parameter. These pulses afford optimal amplitude modulation, achieving uniform and highly selective profiles at any effective flip angle. In this paper, BASSI pulses are parameterized to obtain low RF energy pulsed arterial spin labeling (PASL) label pulses with minimal contamination of static spins outside the label region and highly selective PICORE/QUIPSS II saturation pulses allowing for small label gaps. They are compared to frequency offset corrected inversion (FOCI) label pulses and sinc saturation pulses in simulations and a phantom experiment. Drawing on the outstanding selectivity of bandwidth-modulated saturation pulses, a new noninvasive method to measure in vivo the contamination effects due to direct and indirect saturation of static spins by the label pulse is presented. In an in vivo study on four subjects, contamination effects in a QUIPSS II PASL implementation based on BASSI pulses are compared to those present in a state-of-the-art Q2TIPS sequence employing a FOCI label pulse. Residual contamination in the QUIPSS II/BASSI sequence is shown to be reduced by a factor of 3, compared to the Q2TIPS/FOCI sequence. In vivo human perfusion images obtained with a label gap of only 2 mm are presented.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".