The wall-filter selection curve method for objective tuning of power Doppler clutter filter cutoff velocity.
Bibliographic record
Abstract
High-frequency power Doppler ultrasound is commonly used to assess vascularity in small-animal cancer models, but quantitative images can be difficult to obtain in the presence of clutter artifacts. To improve vascular quantification, the color pixel density (CPD) in a region of interest can be plotted as a function of wall-filter cutoff velocity to produce the wall-filter selection curve. A mathematical model based on receiver operating characteristic statistics was developed to guide the interpretation of wall-filter selection curves. Mathematical predictions were tested using a VisualSonics Vevo 770 system with a 30-MHz transducer and a flow phantom containing four 200–300-μm-diameter vessels. The phantom mimicked vessel configurations observed in micro-CT images of a transgenic mouse prostate cancer model. Selection curves characteristically include a plateau whose CPD may correspond to either the total vascular volume fraction or to the volume fraction of a subset of vessels in the region. The flow-phantom data indicate that the plateau provides a reliable estimate of total vascularity if the plateau begins at a cutoff velocity <2 mm/s and is longer than 0.5 mm/s. The wall-filter selection curve may enable adaptation of scanner settings to changing flow conditions as a tumor progresses during a longitudinal study.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.005 | 0.002 |
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".