Blood oxygen flux estimation with a combined photoacoustic and high-frequency ultrasound microscopy system: a phantom study
Bibliographic record
Abstract
The metabolic rate of oxygen consumption, an important indicator of tissue metabolism, can be expressed as the change of net blood oxygen flux into and out of a tissue region per 100 g of tissue. In this work, we propose a photoacoustic and Doppler ultrasound method for imaging local blood oxygen flux of a single vessel. An imaging system for combined photoacoustic and high-frequency ultrasound microscopy is presented. This system uses a swept-scan 25-MHz ultrasound transducer with confocal dark-field laser illumination optics. A pulse-sequencer enables ultrasonic and laser pulses to be interlaced so that photoacoustic and Doppler ultrasound images are co-registered. Since the mean flow speed can be measured by color Doppler ultrasound, the vessel cross-sectional area can be measured by power Doppler or structural photoacoustic imaging, and multi-wavelength photoacoustic methods can be used to estimate oxygen saturation (sO(2)) and total concentration of haemoglobin (C(Hb)), all of the parameters necessary for oxygen flux estimation can be provided. The accuracy of the flow speed and sO(2) estimation has been investigated. In vitro sheep blood phantom experiments have been performed at different sO(2) levels and mean flow speeds. Blood oxygen flux has been estimated, and the uncertainty of the measurement has been quantified.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".