Using variable‐rate selective excitation (VERSE) radiofrequency pulses to reduce power deposition in pulsed arterial spin labeling sequence at 7 Tesla
Bibliographic record
Abstract
PURPOSE: Arterial spin labeling (ASL) is an established noninvasive MRI technique used for cerebral blood flow measurement, which generally suffers from a low signal-to-noise ratio (SNR). The use of ultra-high fields to enhance sensitivity inevitably results in an increase in TR because of specific absorption rate (SAR) constraints, causing inadequate sampling of hemodynamic response in functional MRI, and adversely affecting concurrent measurement such as blood oxygen level dependent. To address this problem, variable-rate selective excitation (VERSE) radiofrequency (RF) pulses were used. METHODS: The conventional (sinc) selective RF pulses of the Q2TIPS block in the PICORE pulsed ASL (PASL) sequence used for blood saturation were replaced by their equivalent VERSE RF waveforms. Nine healthy volunteers were scanned using the conventional and VERSE PASL sequences on a head-only 7T scanner operating in parallel transmit mode. RESULTS: VERSE PASL sequence provides perfusion images similar to the conventional version, with comparable perfusion SNR (conventional, 3.33 ± 0.48; VERSE, 3.26 ± 0.55) and temporal SNR (conventional, 1.02 ± 0.20; VERSE, 1.05 ± 0.12) for TR = 3.5 seconds and 70 measurements. With shorter acquisition time (TR = 2.5 seconds), VERSE PASL sequence still provides similar results to those acquired using the conventional PASL sequence with longer TR = 3.5 seconds. CONCLUSION: The use of VERSE RF pulses in the Q2TIPS block of a PASL sequence allowed for the reduction of RF power deposition and, consequently, an increase in the temporal resolution and/or perfusion SNR.
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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) | 0.002 | 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".