When Perfusion Meets Diffusion: <i>in vivo</i> Measurement of Water Permeability in Human Brain
Bibliographic record
Abstract
Quantification of water permeability can improve the accuracy of perfusion measurements obtained with arterial spin labeling (ASL) methods, and may provide clinically relevant information regarding the functional status of the microvasculature. The amount of labeled water in the vascular and tissue compartments in an ASL experiment can be estimated based on their distinct diffusion characteristics, and in turn, water permeability determined from the relative vascular and tissue contributions. In the present study, a hybrid magnetic resonance imaging technique was introduced by marrying a continuous ASL method with a twice-refocused spin-echo diffusion sequence. Series of diffusion-weighted ASL signals were acquired with systematically varied b values. The signals were modeled with fast and slow decaying components that were associated with the vascular and tissue compartments, respectively. The relative amount of labeled water in the tissue compartment increased from 61% to 74% and to 86% when the postlabeling delay time was increased from 0.8 to 1.2 and to 1.5 secs. With a b value of 50 secs/mm2, the capillary contribution (fast component) of the ASL signal could be effectively minimized. Using the single-pass approximation model, the water permeability of gray matter in the human brain was estimated based on the derived relative water fractions in the tissue and microvasculature. The potential for in vivo magnetic resonance mapping of water permeability was showed using two diffusion weighted ASL measurements with b=0 and 50 secs/mm2 in both healthy subjects and a case of brain tumor.
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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 | 0.001 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".