Robust method for 3D arterial spin labeling in mice
Bibliographic record
Abstract
Arterial spin labeling is a versatile perfusion quantification methodology, which has the potential to provide accurate characterization of cerebral blood flow (CBF) in mouse models. However, a paucity of physiological data needed for accurate modeling, more stringent requirements for gradient performance, and strong artifacts introduced by magnetization transfer present special challenges for accurate CBF mapping in the mouse. This article describes robust mapping of CBF over three-dimensional brain regions using amplitude-modulated continuous arterial spin labeling. To provide physiological data for CBF modeling, the carotid artery blood velocity distribution was characterized using pulsed-wave Doppler ultrasound. These blood velocity measurements were used in simulations that optimize inversion efficiency for parameters meeting MRI gradient duty cycle constraints. A rapid slice positioning algorithm was developed and evaluated to provide accurate positioning of the labeling plane. To account for enhancement of T(1) due to magnetization transfer, a binary spin bath model of magnetization transfer was used to provide a more accurate estimate of CBF. Finally, a study of CBF was conducted on 10 mice with findings of highly reproducible inversion efficiency (mean ± standard-error-of-the-mean, 0.67 ± 0.03), statistically significant variation in CBF over 12 brain regions (P < 0.0001) and a mean ± standard-error-of-the-mean whole brain CBF of 219 ± 6 mL/100 g/min.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".