Non-contact photoacoustic tomography and ultrasonography for biomedical imaging
Bibliographic record
Abstract
Photoacoustic tomography (PAT) and ultrasonography (US) of biological tissues usually rely on ultrasonic transducers for the detection of ultrasound. For an optimum sensitivity, transducers require a physical contact with the tissue using a coupling fluid (water or gel). Such a contact is a major drawback in important potential applications such as surgical procedures on human beings and small animal imaging in research laboratories. On the other hand, laser ultrasonics (LU) is a well established optical technique for the non-contact generation and detection of ultrasound in industrial materials. In this paper, the remote optical detection scheme used in industrial LU is adapted to allow the detection of ultrasound in biological tissues while remaining below laser exposure safety limits. Both non-contact PAT (NCPAT) and non-contact US (NCUS) are considered experimentally using a high-power single-frequency detection laser emitting suitably shaped pulses and a confocal Fabry-Perot interferometer in differential configuration. It is shown that an acceptable sensitivity is obtained while remaining below the maximum permissible exposure (MPE) of biological tissues. Results were obtained ex vivo on chicken breast specimens with embedded inclusions simulating blood vessels optical properties. Sub-mm inclusions are readily detected at depths approaching 1 cm. The method is expected to be applicable to living tissues.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".