Quantification Issues in Arterial Spin Labeling Perfusion Magnetic Resonance Imaging
Bibliographic record
Abstract
Arterial spin labeling (ASL) perfusion magnetic resonance imaging has gained wide acceptance for its value in clinical and neuroscience applications during recent years. Its capability for noninvasive and absolute perfusion quantification is a key characteristic that makes ASL attractive for many clinical applications. In the present review, we discuss the main parameters or factors that affect the reliability and accuracy of ASL perfusion measurements. Our secondary goal was to outline potential solutions that may improve the reliability and accuracy of ASL in clinical settings. It was found that, through theoretical analyses, flow quantification is most sensitive to tagging efficiency and estimation of the equilibrium magnetization of blood signal (M(0b)). Variations of blood T1 have a greater effect on perfusion quantification than variations of tissue T1. Arterial transit time becomes an influential factor when it is longer than the postlabeling delay time. The T2's of blood and tissue impose minimal effects on perfusion calculation at field strengths equal to or lower than 3.0 T. Subsequently, we proposed various approaches for in vivo estimation or calibration of the above parameters, such as the use of phase-contrast magnetic resonance imaging for calibration of the labeling efficiency as well as the use of inversion recovery TrueFISP (true fast imaging with steady-state precession) sequence for blood T1 mapping. We also list representative clinical cases in which implicit assumptions for ASL perfusion quantification may be violated, such as the venous outflow effect in children with sickle cell disease. Finally, an optimal imaging protocol including in vivo measurements of several critical parameters was recommended for clinical ASL 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 | 0.014 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".