A time-resolved subtraction method for evaluating the optical properties of layered turbid media(Conference Presentation)
Bibliographic record
Abstract
The analysis of statistical moments of time-resolved (TR) diffuse optical signals can be used to evaluate the absorption and scattering coefficients of turbid media; however, this method requires careful measurement of the instrument response function. We propose an alternative approach that avoids this step by estimating the optical properties from the difference of TR measurements acquired at different source-detector separations. The efficiency of this method was validated using simulated data (from analytical model and Monte-Carlo simulations) and tissue-mimicking phantoms. Results for a homogenous and layered medium showed that the subtraction technique can accurately estimate the optical properties. Specifically, our preliminary results show that the method can estimate the optical properties of a homogeneous medium (simulated using μa = 0.1 mm-1, μs’ = 10 mm-1) with an error less than 10 %. Accurate results were obtained at source-detector separations large enough (5 mm or greater) to resolve differences in the moments. Moreover, we also observed that the subtraction method has improved depth sensitivity compared to the classic method of moments. These results suggests that time-resolved subtraction is a simple but effective means of quantifying optical properties of turbid media, in addition to offering a new approach for obtaining spatially sensitive measurements, although additional studies are required to confirm the latter.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".