Improved method for objective selection of power Doppler wall filter cut-off velocity for microvascular imaging.
Bibliographic record
Abstract
The wall-filter selection curve method has been enhanced to improve detection and interpretation of color pixel density (CPD) plateaus. The improved algorithm was developed by analyzing data acquired from three fields of view in a four-vessel flow phantom using a 30-MHz swept-scan transducer. An N-point maximum envelope peak search applied to the first difference of CPD detects selection curve plateaus by incorporating criteria that identify intervals of minimum variation in CPD. Selection curves for regions of interest (ROIs) containing multiple vessels can be difficult to interpret, so the algorithm subdivides the image into small ROIs, constructs selection curves for each ROI, and sums the resulting vascularity estimates. The lower limit on ROI size is constrained by a need to avoid ROIs that are completely filled by blood. A multiple-step decision algorithm was designed that considers the number, length, and slope of each plateau to identify the cutoff velocity that yields the best vascularity estimate. At high (> 5 mm/s) flow velocities, the decision algorithm yielded a summed CPD that was within 5% of the vascular volume fraction in each field of view. These improvements are an initial step toward automating wall-filter cutoff settings in a power Doppler system.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".