Estimating vascular volume fraction in a network of small blood vessels with high-frequency power Doppler ultrasound
Bibliographic record
Abstract
Quantitative images of high-frequency (> 20 MHz) power Doppler ultrasound can be difficult to obtain in the presence of flow artifacts due to power Doppler's sensitivity to operator-dependent acquisition settings. To improve flow quantification, color pixel density (CPD) can be plotted as a function of wall filter cut-off velocity to produce a wall-filter selection curve that can be used to estimate vascular volume fraction by locating the plateau along the curve. The behavior of the wall-filter selection curve in a multiple-vessel region of interest is studied using a custom-designed multiple-vessel flow phantom. The flow phantom is capable of mimicking a range of blood vessel sizes (200-300 microm), blood flow velocities (1-10 mm/s), and blood vessel orientations (long-axis and transverse). At high flow rates, single-vessel wall-filter selection curves superimpose to produce a multiple-vessel curve where the CPD at the left-most plateau corresponds with the actual vascular volume fraction. However, interpretation of the multiple-vessel wall-filter selection curve is not straightforward when the flow rate in the vascular network is low.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".