Photoacoustic and Doppler ultrasound for oxygen consumption estimation: implementation on a clinical array system
Bibliographic record
Abstract
Recently, we have developed a combined photoacoustic and high-frequency Doppler ultrasound system with a single element transducer to estimate the metabolic rate of oxygen consumption in small animal models. However, the long scanning time due to mechanical motion may be a limitation of our swept-scan system. In this work, the single element transducer was replaced by a clinical array transducer which may provide more accurate flow velocity estimations, higher frame rates, improved penetration depth, and improved depth-of-field due to dynamic focusing capabilities. We used an array system from Verasonics Inc. which enables flexible pulse-sequence programming and parallel channel data acquisition, along with a pulsed laser and optical parametric oscillator. For flow estimation, we implemented a flash- Doppler sequence which transmits ensembles of plane-wave excitations. Echo signals are beamformed and subjected to wall-filtering and Kasai flow estimation algorithms. High frame rates over a wide region can be achieved. Combined interlaced photoacoustic and Doppler imaging on flow phantoms has been performed on this system. We demonstrate the ability to image animal blood to depths of 1.5-cm with high signal-to-noise with both modalities. The light penetration is 2-cm. We discuss the performance of Doppler flow estimation and photoacoustic oxygen saturation estimation and their role in future work of estimating oxygen consumption.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".