Angular-domain imaging of fluorescence sources within tissue phantoms
Bibliographic record
Abstract
Conventional fluorescence imaging often does not have a mechanism to remove the scattering effect in biological tissue. We use Angular Domain Imaging (ADI) to improve the detection of smaller structures in fluorescence layer over that can be provided by existing systems. ADI is a high resolution, ballistic imaging method that utilizes the angular spectrum of photons to filter multiple-scattered photons and accepts only photons with small angular deviation from their original trajectory. Advantages of the ADI technique are that it is insensitive to wavelength and the sources are not required to be high quality, coherent, or pulse, as with OCT or time domain. Our target is to perform fluorescence ADI at shallow tissue such as skin (≈ 1mm) with a buried collagen layer. To experimentally model shallow tissue with phantoms, a thin layer of scattering medium with similar scattering characteristic (μs = 200cm-1, g = 0.85) is placed on top fluorescence plastic (415nm excitation, ≈ 555-585nm emission) which is patterned by strips of non-emitting structures (200-400μm). Positioning multiple collimated arrays with acceptance angles of 5.71° on top of the scattering medium, test structures (200μm wide) can be detected at shallow scattering medium thickness (1mm). Monte Carlo simulation confirms that fluorescence ADI can image structures at shallow tissue depth by using collimator array with modest filtration angles. Results show micromachined collimator arrays provide both high spatial resolution and angular filtration on scattered photons.
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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 | 0.000 | 0.001 |
| 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) | 0.002 | 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".