Assessment of Opto-mechanical Behavior of Biological Samples by Interferometry
Bibliographic record
Abstract
Optoacoustic imaging is a relatively novel biomedical imaging modality that relies on the absorption of light to create pressure transients that can be detected ultrasonically. In most scientific communications, the source of tissue contrast has been described as primarily optical. However, the thermomecahnical properties of tissue, as expressed through the Gruneisen coefficient, also affect the optoacoustic signal. To investigate the effect of thermomechanical tissue properties short pulses (~ 6.5 ns) from an optical parametric oscillator at 750 nm were used to irradiate coagulated and uncoagulated tissue-mimicking albumen phantoms, to emulate normal tissue and tissue that has been heated. The phantoms respond to the laser-induced stress by thermoelastic expansion. This thermomechanical behavior of the samples was assessed using an interferometric system capable of measuring transient displacements with a temporal resolution of less than 10 ns and a spatial resolution of < 10 nm. The experimental measurement allowed determination of the Gruneisen coefficient which is an important thermo-mechanical sample property that can affect generation of optoacoustic signals. An increase in the value of Gruneisen coefficient of 65% was measured when phantoms were coagulated compared to uncoagulated phantoms, consistent with the stiffening of the tissue mimicking material. This suggests that for thermal therapy the changes in the Gruneisen coefficient are also an important source of optoacoustic contrast.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".