Objective Selection of High-Frequency Power Doppler Wall Filter Cutoff Velocity for Regions of Interest Containing Multiple Small Vessels
Bibliographic record
Abstract
High-frequency (> 20 MHz) power Doppler ultrasound is frequently used to quantify vascularity in preclinical studies of small animal angiogenic models, but quantitative images can be difficult to obtain in the presence of flow artifacts. To improve flow quantification, color pixel density (CPD) can be plotted as a function of wall filter cutoff velocity to produce a wall-filter selection curve that can be used to estimate actual vascular volume fraction. A mathematical model based on receiver operating characteristic statistics is developed to study the behavior of wall-filter selection curves. The model is compared to experimental data acquired with a 30-MHz transducer and a custom-designed multiple-vessel flow phantom capable of mimicking a range of blood vessel sizes (200-300 microm), blood flow velocities (1-10 mm/s), and blood vessel orientations. At high flow rates, wall-filter selection curves for multiple-vessel regions include a plateau whose CPD corresponds with the total vascular volume fraction. Conversely, the vascular volume fraction of a subset of vessels is obtained at low flow rates. Detection of the volume fraction of all vessels is ensured when a plateau is > 0.5 mm/s in length and begins at a wall filter cutoff < 2 mm/s.
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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 | 0.002 | 0.005 |
| 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 | 0.001 | 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".