Determination of the optical properties of turbid media using relative interstitial radiance measurements: Monte Carlo study, experimental validation, and sensitivity analysis
Bibliographic record
Abstract
Interstitial quantification of the optical properties of tissue is important in biomedicine for both treatment planning of minimally invasive laser therapies and optical spectroscopic characterization of tissues, for example, prostate cancer. In a previous study, we analyzed a method first demonstrated by Dickey et al., [Phys. Med. Biol. 46, 2359 (2001)] to utilize relative interstitial steady-state radiance measurements for recovering the optical properties of turbid media. The uniqueness of point radiance measurements were demonstrated in a forward sense, and strategies were suggested for improving performance under noisy experimental conditions. In this work, we test our previous conclusions by fitting the P3 approximation for radiance to Monte Carlo predictions and experimental data in tissue-simulating phantoms. Fits are performed at: 1. a single sensor position (0.5 or 1 cm), 2. two sensor positions (0.5 and 1 cm), and 3. a single sensor position (0.5 or 1 cm) with input knowledge of the sample's effective attenuation coefficient. The results demonstrate that single sensor radiance measurements can be used to retrieve optical properties to within approximately 20%, provided the transport albedo is greater than approximately 0.9. Furthermore, compared to the single sensor fits, employing radiance data at two sensor positions did not significantly improve the accuracy of recovered optical properties. However, with knowledge of the effective attenuation coefficient of the medium, optical properties can be retrieved experimentally to within approximately 10% for an albedo greater or equal to 0.5.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".