Integrated photoacoustic and oblique incidence diffuse reflectance system for quantitative optical sensing in turbid media
Bibliographic record
Abstract
The photoacoustic signal of an optical absorber in a turbid medium is proportional to the local laser fluence, the optical absorption coefficient and the Gruneisen parameter. The local fluence at a subsurface absorber is determined by the initial incident fluence and optical properties of the media. Knowledge of laser fluence at subcutaneous tissue locations will improve our ability to estimate local chromophore concentrations and will lead to more quantitative estimates of blood oxygen saturation with photoacoustics. By integrating an oblique incidence reflectance (OIR) system in a photoacoustic imaging system, we are able to estimate optical properties of the turbid medium. To do this, we use a unique photoacoustic probe consisting of a 45-degree optical prism in an optical index-matching fluid. An oblique CWlaser beam interrogates the tissue surface at the same location as a pulsed laser, used for photoacoustic interrogation. Photoacoustic signals collected from the tissue are deflected by the prism to a focused 10 MHz ultrasound transducer. Diffuse light from the CW-laser is collected by a CCD camera and analyzed to estimate the bulk absorption and scattering coefficients. We fixed a tube filled with known concentrations of an absorbing dye below the probe in an Intralipid bath. We obtained the OIR and photoacoustic measurements for different Intralipid concentrations (providing a μs' between 1 and 10 cm-1). The OIR measurements were used to estimate the bulk optical parameters. Using these values, models of light transport were then used to calculate the local laser fluence to normalize the photoacoustic measurements. The corrected photoacoustic signals show direct proportionality to the tube dye concentrations irrespective of bulk turbid medium properties.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| 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) | 0.006 | 0.003 |
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".