Understanding quantification of microvascularity with high-frequency power Doppler ultrasound
Bibliographic record
Abstract
High-frequency power Doppler imaging of angiogenesis can be challenging given the presence of small blood vessels and slow flow velocities. In the presence of substantial Doppler artifacts such as false-positive color pixels or undetected vessels, color pixel density (CPD) and related vascularity metrics do not provide accurate estimates of vascular volume fraction. As a step towards improved microvascular quantification, flow-phantom experiments were performed to establish relationships between CPD and wall filter cut-off velocity for various combinations of vessel size (160, 200, 250, 300, and 360 μm), flow velocity (4, 3, 2, 1, and 0.5 mm/s), and transducer frequency (30 and 40 MHz). Three distinct regions were observed in plots of CPD versus wall filter cut-off velocity: overestimation of CPD at low cut-offs, underestimation of CPD at high cut-offs, and a plateau at intermediate cut-offs. The CPD at the plateau closely matched the phantom's actual vascular volume fraction. The length of the plateau corresponded with the flow-detection performance of the Doppler system, which was assessed using receiver operating characteristic analysis. Color pixel density versus wall filter cut-off curves from analogous in vivo experiments exhibited the same shape, including a distinct CPD plateau. The similar shape of the flow-phantom and in vivo curves suggests that the presence of a plateau can be used to identify the best-estimate CPD value in an in vivo experiment. The ability to identify the best CPD estimate is expected to improve quantification of angiogenesis and anti-angiogenic treatment responses with power Doppler.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".