Fast scattering coefficient measurement of intralipid-infused tissue phantoms using imaging sensors
Bibliographic record
Abstract
The time instability of regular biological tissue makes repeatable optical tomography imaging experiments through tissue difficult, hence the need for long life tissue phantoms with adjustable scattering parameters. Our test phantoms employ a wide density range of intralipid-infused agar layers 1-6 mm thick building phantoms with small to large scattering parameters of values μs =< 20cm<sup>-1</sup>, g = 0.95. The intralipid-infused agar is encapsulated within clear polymer stabilizing these for >10 years creating samples with ranges of thicknesses, scattering characteristics and shapes. To tune phantoms we have created an improved rapid measurement method for scattering coefficients μs and anisotropy factor g. Using a DSLR camera with full frame 36x24mm sensor we 3D printed an optical jig which mounts phantoms like a lens to the camera. Aligning laser beams to the phantom a single picture captures ~6 million pixel values over +/-12°, creating 20,000 measurements each at 2300 angular bins of 0.005°. Matlab programs calculate the scattering center and concentric circles of pixels at each angular position. Nonlinear curve fitting the two term Henyey-Greenstein model extracts the pairs of HG parameters: weighing factors, μs and g parameters. Fits are highly statistical significance with exceedingly small deviation from the HG model. Each of 3 test phantom were measured a wide wavelengths range: 405, 532, 632, 670 and NIR 808 nm. The heat mirrors in regular DSLR/Mirror cameras still allow NIR measurements (to ~1000 nm) due to the very low noise of these photography systems.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".