P2B-5 Quantification of Flow Using Ultrasound and Microbubbles: A Disruption Replenishment Model Based on Physical Principles
Bibliographic record
Abstract
With contrast agents, ultrasound can make hemodynamic measurements in microvascular networks with the technique of disruption replenishment. In its current form, the method suffers from poor reproducibility and accuracy, largely due to the inappropriate use of a mono-exponential model for fitting the time replenishment data. In reality, the time-intensity replenishment curve reflects the hemodynamics and morphology of the vascular system being measured, the ultrasound field distribution and microbubble properties. Here, we introduce an analytic replenishment model that attempts to account for these parameters and compare its performance to the established model in a flow phantom. Specifically, the proposed model 1) incorporates the hemodynamic properties of the flow system (velocity distribution and vascular cross section), 2) includes the elevation and axial plane pressure distributions and 3) accounts for the distinct high and low MI disruption and detection boundaries. Compared to the currently accepted mono-exponential model, the presented model shows better agreement in both the quality of the fit and estimation of velocity (~5-10% vs. 20% error) for the same flow and acoustic conditions
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".