Combined photoacoustic and high-frequency power Doppler ultrasound imaging
Bibliographic record
Abstract
Photoacoustic imaging has emerged as a promising technique for visualizing optically absorbing structures with ultrasonic spatial resolution. Since it relies on optical absorption of tissues, photoacoustic imaging is particularly sensitive to vascular structures even at the micro-scale. Power Doppler ultrasound can be used to detect moving blood irrespective of Doppler angles. However, the sensitivity may be inadequate to detect very small vessels with slow flow velocities. In this work, we merge these two synergistic modalities and compare power Doppler ultrasound images with high-contrast photoacoustic images. We would like to understand the advantages and disadvantages of each technique for assessing microvascular density, an important indicator of disease status. A combined photoacoustic and highfrequency ultrasound system has been developed. The 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. Experiments have been performed on flow phantoms to test the capability of our system and signal processing methods. Work in progress includes in vivo color flow mapping. This combined system will be used to perform blood oxygen saturation and flow estimations, which will provide us with the parameters to estimate the local rate of metabolic oxygen consumption, an important indicator for many diseases.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".