Characterizing long lived intralipid-infused tissue phantoms scattering using imaging sensors
Bibliographic record
Abstract
Since regular biological tissue’s optical characteristics are unstable building long lived test phantoms that simulate their scattering characteristics are needed for testing optical methods of imaging through tissue. Our ongoing work developed a methodology to build long term stable phantoms with lifetimes >6 years which can be tuned to maintaining optical characteristics mimicing skin characteristics and a rapid technique to measure the scattering coefficients μs and anisotropy factor g. Our test phantoms employ intralipid-infused agar layers 1 to 8 mm thick. Agra can take a wide range of intralipid concentrations enabling the building phantoms of a large range of scattering parameters with typical values of μs=20cm-1, g=0.95. Encapsulating the intralipid-infused agar within a clear polymer has proved to stabilize these for long lifetimes and allows creation of varying thicknesses, scattering characteristics and shapes. We developed a rapid technique where the light from a laser beam passing perpendicular through the test phantom is captured using a 36×24mm digital camera sensor. This gathers ~6×106 measurements over a ±12° range, giving ~20,000 points each at 2300 angular bins of 0.005° . A Matlab program identifies the scattering center and data points for each angular position. Using a HenyeyGreenstein two-term model a nonlinear curve fitting extracts pairs of HG weighing factors, µs and g parameters. Fit results show extremely high statistical significance with exceedingly small deviation from the HG model for multiple wavelengths (currently 533, 632, 670 nm lasers and a supercontinuum laser at 650, 700,750 nm) for several test phantoms.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".