Advancing 7T perfusion imaging by pulsed arterial spin labeling: Using a parallel transmit coil for enhanced labeling robustness and temporal SNR
Bibliographic record
Abstract
Non-invasive perfusion imaging by Arterial spin labeling (ASL) can be advantageous at Ultra-high field (UHF) MRI, since the image SNR and the T1 relaxation time both increase with the static field. However, ASL implementation, especially at 7T, is not trivial. Especially for ASL, UHF MRI comes with many challenges, mainly due to B1+ inhomogeneities. This study aimed to investigate the effects of different transmit coil configurations on perfusion-weighted imaging at 7T using a flow-sensitive alternating inversion recovery (FAIR) technique with time-resolved frequency offset corrected inversion (TR-FOCI) pulses for labeling and background suppression. We conducted a performance comparison between a parallel transmit (pTx) system equipped with 32 receive (Rx) and 8 transmit (Tx) channels and a standard setup with 32Rx and 2Tx channels. Our findings demonstrate that the pTx system, characterized by a more homogeneous B1 transmit field, resulted in a significantly higher contrast-to-noise ratio, temporal signal-to-noise ratio, and lower coefficient of variance (CoV) than the standard 2Tx setup. Additionally, both setups demonstrated comparable capabilities for functional mapping of the hand region in the motor cortex, achieving reliable results within a short acquisition time of approximately 5 minutes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".