Objective Selection of High-Frequency Power Doppler Wall Filter Cutoff Velocity for Regions of Interest Containing Multiple Small Vessels
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it