Quantification of <scp>blood–brain</scp> barrier water exchange and permeability with multidelay diffusion‐weighted <scp>pseudo‐continuous arterial spin labeling</scp>
Bibliographic record
Abstract
Purpose To present a pulse sequence and mathematical models for quantification of blood–brain barrier water exchange and permeability. Methods Motion‐compensated diffusion‐weighted (MCDW) gradient‐and‐spin echo (GRASE) pseudo‐continuous arterial spin labeling (pCASL) sequence was proposed to acquire intravascular/extravascular perfusion signals from five postlabeling delays (PLDs, 1590–2790 ms). Experiments were performed on 11 healthy subjects at 3 T. A comprehensive set of perfusion and permeability parameters including cerebral blood flow (CBF), capillary transit time (τc), and water exchange rate (kw) were quantified, and permeability surface area product (PSw), total extraction fraction (Ew), and capillary volume (Vc) were derived simultaneously by a three‐compartment single‐pass approximation (SPA) model on group‐averaged data. With information (i.e., Vc and τc) obtained from three‐compartment SPA modeling, a simplified linear regression of logarithm (LRL) approach was proposed for individual kw quantification, and Ew and PSw can be estimated from long PLD (2490/2790 ms) signals. MCDW‐pCASL was compared with a previously developed diffusion‐prepared (DP) pCASL sequence, which calculates kw by a two‐compartment SPA model from PLD = 1800 ms signals, to evaluate the improvements. Results Using three‐compartment SPA modeling, group‐averaged CBF = 51.5/36.8 ml/100 g/min, kw = 126.3/106.7 min−1, PSw = 151.6/93.8 ml/100 g/min, Ew = 94.7/92.2%, τc = 1409.2/1431.8 ms, and Vc = 1.2/0.9 ml/100 g in gray/white matter, respectively. Temporal SNR of MCDW‐pCASL perfusion signals increased 3‐fold, and individual kw maps calculated by the LRL method achieved higher spatial resolution (3.5 mm3 isotropic) as compared with DP pCASL (3.5 × 3.5 × 8 mm3). Conclusion MCDW‐pCASL allows visualization of intravascular/extravascular ASL signals across multiple PLDs. The three‐compartment SPA model provides a comprehensive measurement of blood–brain barrier water dynamics from group‐averaged data, and a simplified LRL method was proposed for individual kw quantification.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".