Fast-scanning ultrasonic-photoacoustic biomicroscope: in vivo performance
Bibliographic record
Abstract
The combination of ultrasonic and photoacoustic imaging modalities has yet to be realized in the high-frequency regime (>20MHz) where spatial resolution may permit visualization of the microvasculature. In this work, we characterize the in-vivo performance of a custom ultrasound-photoacoustic B-scanning imaging system. This system utilizes a combined ultrasound/photoacoustic probe attached to a voice-coil capable of approximately 1cm lateral translation at a rate of up to 15Hz. The probe is comprised of a 25MHz ultrasound transducer, configured confocally with a conical mirror-based dark-field laser delivery system. The fast-scanning mode permits realtime ultrasound imaging. The imaging speed of the photoacoustic mode is limited by the repetition rate of the 532nm laser (up to 20Hz). Signals from the transducer are amplified by a 39dB preamp with an additional time-gain compensation stage of up to 24dB. Control of the system is through a digital input-output PCI card, which acts as a pulse-sequencer and permits software control of time-gain compensation. This setup permits interlaced pulse sequences for excellent registration of ultrasonic and photoacoustic data, as well as separate timegain compensation curves for photoacoustic and ultrasound modalities. We have managed to achieve a lateral resolution of 155 μm and an axial resolution of 40 μm. The system is used to visualize the finger and palm of a hand to almost 1cm ultrasound depths and multiple millimeter-scale photoacoustic depths. Photoacoustic images are overlaid on the ultrasound images for simultaneous visualization of the microvasculature and surrounding tissue.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".