The application of frequency-domain photoacoustics to temperature-dependent measurements of the Grüneisen parameter in lipids
Bibliographic record
Abstract
The Grüneisen parameter is an essential factor in biomedical photoacoustic (PA) diagnostics. In most PA imaging applications, the variation of the Grüneisen parameter with tissue type is insignificant. This is not the case for PA imaging and characterization of lipids, as they have a very distinct Grüneisen parameter compared with other tissue types. One example of PA applications involving lipids is the imaging and characterization of atherosclerotic plaques. Intravascular photoacoustic (IVPA) imaging is a promising diagnostic tool that can evaluate both plaque severity and composition. The literature for IVPA has mainly focused on using the difference in absorption coefficients between plaque components and healthy arterial tissues. However, the Grüneisen parameters for lipids and their behavior with temperature have not been well established in the literature. In this study we employ frequency-domain photoacoustic measurements to estimate the Grüneisen parameter by virtue of the ability of this modality to independently measure both the absorption coefficient and the Grüneisen parameter through the use of the phase channel. The values of the Grüneisen parameters of some lipids are calculated as functions of temperature in the range 25–45 °C.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".