Reflection-mode multiple-illumination photoacoustic sensing to estimate optical properties
Bibliographic record
Abstract
OBJECTIVES: We analyze a reflection-mode multiple-illumination photoacoustic method which allows us to estimate optical scattering properties of turbid media based on fitting light-transport models and explore its limits in optical property estimation and depth-dependent fluence compensation. BACKGROUND: Recent simulation results show significant promise for a technique called multiple-illumination photoacoustic tomography (MI-PAT) to quantitatively reconstruct both absorption and scattering heterogeneities in turbid medium. Prior to experiments, it is essential to develop and analyze a measurement technique and probe capabilities of quantitative measurements that focus on sensing rather than imaging. METHODS: This technique involved translation of a 532 nm pulsed-laser light spot while focusing an ultrasound receiver on a sub-surface optical absorber immersed in a scattering medium at 3, 4 and 5 mm below the surface. Measured photoacoustic amplitudes for media with different reduced scattering coefficients are fitted with a light propagation model to estimate optical properties. RESULTS: When the absorber was located at 5 mm below the membrane in media with a reduced scattering coefficient of 4.4 and 5.5 cm(-1), the true values were predicted with an error of 5.7% and 12.7%, respectively. We observe accuracy and the ability of estimating optical scattering properties decreased with the increased reduced scattering coefficient. Nevertheless, the estimated parameters were sufficient for demonstrating depth-dependent fluence compensation for improved quantitation in photoacoustic 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 | 0.001 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".