Investigation of Phase‐Change Droplets and Fast Imaging for Indicator Dilution Measurement of Flow
Bibliographic record
Abstract
OBJECTIVES: The development of low boiling point liquid droplets as phase-change contrast agents allows for the local creation of microbubbles at a point of interest in vivo. Although there are many possible applications, few investigations have used selectively created microbubble boluses to measure volumetric flowrate. In this study, the flow ratio between two vessels is calculated by vaporizing droplets in each vessel individually. METHODS: Proof of principle is demonstrated in vitro by an imaging sequence that vaporizes droplets using a high mechanical index pulse, then images the transit of the resulting microbubbles at a high frame rate using low mechanical index plane waves. RESULTS: It is shown that a linear relationship exists between the concentration of droplets and enhancement of the resulting microbubble bolus. In vitro flow is measured with a mean error of 8% in a 0.66 cm diameter vessel and with a mean error of 33% in a 0.49 cm diameter vessel. The relative volumetric flow between two adjacent vessels is calculated with a mean percentage error of 25% when imaging the region of droplet vaporization for flow ratios between 0.25 and 4. CONCLUSIONS: This in vitro study demonstrates the feasibility of using a positive bolus tracer, induced by image-guided ultrasound excitation, to measure flow. Potential applications include measurement of the portal vein to hepatic artery flow ratio, known as the hepatic perfusion index.
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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) | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".