High frequency b-mode ultrasound blood flow estimation in the microvasculature
Bibliographic record
Abstract
High frequency ultrasound imaging has shown great ability in various applications for visualization of the microcirculation. A new speckle-variance flow processing (SFP) algorithm has been developed for noninvasive estimation of slow blood flow. This technique is based on analyzing the changes of B-mode image intensity along sequences of images. The method has been evaluated on a flow phantom with blood-mimicking fluid in the velocity range from 0.1 mm/s to 30 mm/s. The velocity index estimated with the SFP method shows a linear increase as a function of calibrated flow velocity in the range of 0.1 mm/s to 1.5 mm/s. For larger flow velocities, the SFP index acts as a motion detector. Results have been compared with speckle tracking (ST) flow velocity estimation and give good agreement. Ultrasound image sequences have been processed with the proposed method. This demonstrates the feasibility of mapping slow blood flow in real time with a non-Doppler method.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".