Non-contact biomedical photoacoustic and ultrasound imaging
Bibliographic record
Abstract
The detection of ultrasound in photoacoustic tomography (PAT) usually relies on ultrasonic transducers in contact with the biological tissue through a coupling medium. This is a major drawback for important potential applications such as surgery. Here we report the use of a remote optical method, derived from industrial laser-ultrasonics, to detect ultrasound in tissues. This approach enables non-contact PAT (NCPAT) without exceeding laser exposure safety limits. The sensitivity of the method is based on the use of suitably shaped detection laser pulses and a confocal Fabry-Perot interferometer in differential configuration. Reliable image reconstruction is obtained by measuring remotely the surface profile of the tissue with an optical coherence tomography system. The proposed method also allows non-contact ultrasound imaging (US) by applying a second reconstruction algorithm to the data acquired for NCPAT. Endogenous and exogenous inclusions exhibiting optical and acoustic contrasts were detected ex vivo in chicken breast and calf brain specimens. Inclusions down to 0.3 mm in size were detected at depths exceeding 1 cm. The method could expand the scope of photoacoustic and US to in-vivo biomedical applications where contact is impractical.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".