Reference optical phantoms for diffuse optical spectroscopy Part 1 – Error analysis of a time resolved transmittance characterization method
Bibliographic record
Abstract
Development, production quality control and calibration of optical tissue-mimicking phantoms require a convenient and robust characterization method with known absolute accuracy. We present a solid phantom characterization technique based on time resolved transmittance measurement of light through a relatively small phantom sample. The small size of the sample enables characterization of every material batch produced in a routine phantoms production. Time resolved transmittance data are pre-processed to correct for dark noise, sample thickness and instrument response function. Pre-processed data are then compared to a forward model based on the radiative transfer equation solved through Monte Carlo simulations accurately taking into account the finite geometry of the sample. The computational burden of the Monte-Carlo technique was alleviated by building a lookup table of pre-computed results and using interpolation to obtain modeled transmittance traces at intermediate values of the optical properties. Near perfect fit residuals are obtained with a fit window using all data above 1% of the maximum value of the time resolved transmittance trace. Absolute accuracy of the method is estimated through a thorough error analysis which takes into account the following contributions: measurement noise, system repeatability, instrument response function stability, sample thickness variation refractive index inaccuracy, time correlated single photon counting system time based inaccuracy and forward model inaccuracy. Two sigma absolute error estimates of 0.01 cm(-1) (11.3%) and 0.67 cm(-1) (6.8%) are obtained for the absorption coefficient and reduced scattering coefficient respectively.
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".