Effects of the apparent transverse relaxation time on cerebral blood flow measurements obtained by arterial spin labeling
Bibliographic record
Abstract
Previous modeling studies have predicted that a significant fraction of the signal in arterial spin labeling (ASL) experiments originates from labeled water in the capillaries. Provided that the relaxation times in blood and tissue are similar, ASL data can still be analyzed with the conventional one-compartment Kety model. Such studies have primarily focused on T1 differences and have neglected any differences in transverse relaxation times (T2 and T2*). This is reasonable for studies at lower fields; however, it may not be valid at higher fields due to the stronger susceptibility effects of deoxygenated blood. In this study a tracer kinetic model was developed that includes T2* differences between capillary blood and tissue. The model predicts that a reduction in blood T2* at higher fields will attenuate the capillary contribution to the ASL signal. This in turn causes an underestimation of CBF when ASL data are analyzed with the one-compartment Kety model. We confirmed this prediction by comparing ASL data collected at 1.5 and 4 T, and at multiple gradient echoes (19, 32, 45, and 58 ms). A decrease in resting-state CBF with echo time (TE) was observed at 4 T, but not at 1.5 T. These results suggest that at higher fields AST data should be collected using gradient-echo techniques with short TEs, or with spin-echo techniques. Furthermore, the sensitivity of the CBF measurements to venous T2* may affect the interpretation of concurrent ASL/BOLD studies.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".