Towards microvascular pressure estimation using ultrasound and photoacoustic imaging
Bibliographic record
Abstract
Microvascular pressure drives perfusion in tissues but is difficult to measure. A method is proposed here to estimate relative pressures in microvessels using photoacoustic and ultrasound tracking of small vessels during calibrated tissue compression. A photoacoustic–ultrasound dual imaging transducer is used to directly compress on tissue in vivo . Photoacoustic signals from blood vessels diminish as an external load is applied and eventually reaches a minimum or vanishes when external pressure is sufficiently greater than the internal pressure. Two methods were proposed to estimate relative pressures. In the first approach, vessels were tracked during compression and when the vessel photoacoustic signals vanished below a set threshold, the internal pressures were assigned as the external loading pressure at the respective collapse point. In this approach pressures required to collapse vessel signatures completely were found to be much greater than physiological blood pressures. An alternative approach was to track the cross-sectional area of small vessels with changing external load and fitting the data to a known Shapiro model for thin-walled vessel compression. This approach produced estimates of internal pressures which were much more realistic. Both approaches produced the same rank-ordering of relative pressures of various vessels in vivo . Approaches thus far require future work to become fully quantitative but the present contributions represent steps towards this goal.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".