Spatiofrequency filter in turbid medium enhanced by background scattered light subtraction from a deviated laser source
Bibliographic record
Abstract
Angular Domain Imaging is an optical tomography technique that filters out scattered light by accepting only photons with small deviation angles from their original trajectories. Previously, angular filters of linear collimating array (0.29° acceptance) or spatiofrequency filter of a +50mm lens with a 214um aperture (0.25° acceptance) were used. In the linear collimating array system, using a wedge prism to deviate the light source by 2-3x the acceptance angle creates a second image of only the scattered components which can then be subtracted from the filtered image to enhance detectability. We now apply this technique to the spatiofrequency filter system at an angle 2x the acceptance. Utilizing several wavelengths of laser sources with different beam symmetries, test phantoms are placed in a 5cm thick sample of diluted intralipid solution, with a maximum SR of 1.64×106:1 (μs' = 1.8cm-1). By digitally subtracting the background scattered light, test phantoms previously unobservable are now distinguishable. Using background subtraction, the SR limitation of the SFF system improves 3x under full illumination and ~40x under line of light illumination. The improvement under partial illumination is similar to the result using the collimator array, but with resolution limited by the optics used in the system.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".