Multi-scale photoacoustic remote sensing (PARS)(Conference Presentation)
Bibliographic record
Abstract
We introduce a novel multi-scale photoacoustic remote sensing (PARS) imaging system. Our system can provide optical resolution details for superficial structures as well as acoustic resolution for deep-tissue imaging down to 5 cm, in a non-contact setting. PARS system does not require any contact with the sample or ultrasound coupling medium. The optical resolution PARS (OR-OARS) system uses optically focused pulsed excitation with optical detection of photoacoustic signatures using a long-coherence interrogation beam co-focused and co-scanned with the excitation spot. In the OR-PARS initial pressures are sampled right at their subsurface origin where acoustic pressures are largest. The Acoustic resolution PARS (AR-PARS) picks up the surface oscillation of the tissue caused by generated photoacoustic signal using a modified version of Michelson interferometry. By taking advantage of 4-meters polarization maintaining single-mode fiber and a green fiber laser we have generated a multi-wavelength source using stimulated Raman scattering. Remote functional imaging using this multi-wavelength excitation source and PARS detection mechanism has been demonstrated. The oxygen saturation estimations are shown for both phantom and in vivo studies. Images of blood vessel structures for an In vivo chicken embryo model is demonstrated. The Phantom studies indicates ~3µm and ~300µm lateral resolution for OR-PARS and AR-PARS respectively. To the best of our knowledge this is the first dual modality non-contact optical and acoustic resolution system used for in vivo imaging.
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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 | 0.000 | 0.000 |
| 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) | 0.012 | 0.004 |
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".