A two‐stage approach for measuring vascular water exchange and arterial transit time by diffusion‐weighted perfusion MRI
Bibliographic record
Abstract
Changes in the exchange rate of water across the blood-brain barrier, denoted k(w), may indicate blood-brain barrier dysfunction before the leakage of large-molecule contrast agents is observable. A previously proposed approach for measuring k(w) is to use diffusion-weighted arterial spin labeling to measure the vascular and tissue fractions of labeled water, because the vascular-to-tissue ratio is related to k(w). However, the accuracy of diffusion-weighted arterial spin labeling is affected by arterial blood contributions and the arterial transit time (τ(a)). To address these issues, a two-stage method is proposed that uses combinations of diffusion-weighted gradient strengths and post-labeling delays to measure both τ(a) and k(w). The feasibility of this method was assessed by acquiring diffusion-weighted arterial spin labeling data from seven healthy volunteers. Repeat measurements and Monte Carlo simulations were conducted to determine the precision and accuracy of the k(w) estimates. Average grey and white matter k(w) values were 110 ± 18 and 126 ± 18 min(-1), respectively, which compare favorably to blood-brain barrier permeability measurements obtained with positron emission tomography. The intrasubject coefficient of variation was 26% ± 23% in grey matter and 21% ± 17% in white matter, indicating that reproducible k(w) measurements can be obtained.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".