Oxygen consumption estimation with combined color doppler ultrasound and photoacoustic microscopy: a phantom study
Bibliographic record
Abstract
The metabolic rate of oxygen consumption (MRO2) quantifies tissue metabolism, which is important for diagnosis of many diseases. For a single vessel model, the MRO2 can be estimated in terms of the mean flow velocity, vessel crosssectional area, total concentration of hemoglobin (CHB), and the difference between the oxygen saturation (sO2) of blood flowing into and out of the tissue region. In this work, we would like to show the feasibility to estimate MRO2 with our combined photoacoustic and high-frequency ultrasound imaging system. 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 velocity can be measured by color Doppler ultrasound, the vessel cross-sectional area can be measured by power Doppler or photoacoustic imaging, and multi-wavelength photoacoustic methods can be used to estimate sO2 and CHB, all of these parameters necessary for MRO2 estimation can be provided by our system. Experiments have been performed on flow phantoms to generate co-registered color Doppler and photoacoustic images. To verify the sO2 estimation, two ink samples (red and blue) were mixed in various concentration ratios to mimic different levels of sO2, and the result shows a good match between the calculated concentration ratios and actual values.
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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 | 0.001 | 0.001 |
| 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".