Enhanced angular domain imaging in turbid media using Gaussian line illumination
Bibliographic record
Abstract
Coherence or Time Domain Optical tomography within highly scattering media observes the shortest path photons over the dominant randomly scattered background light. Angular Domain Imaging employs micromachined collimators detecting photons within small angles of aligned laser light sources. These angular filters are micromachined silicon collimator channels 51 microns wide by 10 mm long on 102 micron spacing, giving an acceptance angle of 0.29 degrees at a CMOS detector array. Phantom test objects were observed in scattering media 5 cm thick at effective scattered to ballistic ratios from 1:1 to greater than 1E8:1. Line and space test objects detection limits are set by detector pixel size (5.5 microns) not collimator hole spacing. To maximize the ballistic/quasi-ballistic photons observed, a line of light aligned with the collimator holes increases detectability by reducing the amount of scattered background light. A Cylindrical Spherical Cylindrical beam expander/shrinker creates a 16 mm by 0.35 mm line of light. Best results occur when the scattering medium, collimator and detector are within 3X the Rayleigh Range of the beam's narrow vertical axis, allowing imaging of 51 micron lines/spaces at 3E8:1 scattering ratios. Restricting the light to a 1 mm line extends this to 8E9:1. Carbon coating the SMCA to reduce reflectivity shows that at high scattering levels absorbing walls will reduce background light, improving contrast. ADI has also been shown to work when the illumination is unaligned with the detector. This allows for side illumination with detection of structures at depths of 3mm with a scattering ratio of 1E6:1.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".