High resolution continuous arterial spin labeling of human cerebral perfusion using a separate neck tagging RF coil
Bibliographic record
Abstract
For standard clinical applications, ASL images are typically acquired with 4-8 mm thick slices and 3-4 mm in-plane resolution. However, in this paper we demonstrate that high-resolution continuous arterial spin labeling (CASL) perfusion images can be acquired in a clinically relevant scan time using current MRI technology. CASL was implemented with a separate neck coil for labeling the arterial blood on a 4.7T MRI using standard axial 2D GE-EPI. Typical-resolution to high-resolution (voxels of 95, 60, 45, 27, or 7 mm3) images were compared for qualitative and quantitative cerebral blood flow analysis (CBF) in nine healthy volunteers (ages: 24-32 years). The highest resolution (1.5x1.5x3 = 7 mm3) CASL implementation yielded perfusion images with improved cortex depiction and increased cortical CBF measurements (53 ± 8 ml/100g/min), consistent with reduced partial volume averaging. The 7 mm3 voxel images were acquired with 6 cm brain coverage in a clinically relevant scan of 6 minutes. Improved spatial resolution facilitates CBF measurement with reduced partial volume averaging and may be valuable for the detection of perfusion deficits in small lesions and perfusion measurement in small brain regions.
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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 | 0.001 |
| 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 | 0.001 |
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".