Multispectral angular domain optical tomography in scattering media with argon and diode laser sources
Bibliographic record
Abstract
Angular Domain Imaging (ADI) within highly scattering media employs micromachined angular filter tunnels to detect nonscattered photons which pass through the tunnels unattenuated while scattered photons collide with the tunnel walls. Each tunnel is micromachined approximately 51 &mgr;m wide by 10 mm long in silicon, giving a maximum acceptance angle of 0.29 degrees. The ADI technique is inherently independent of wavelength, and thus multispectral laser sources can be incorporated. Previous ADI experiments employed a 488-514 nm Argon ion laser source. This paper describes the construction of a new imaging system utilizing a high-power (up to 0.5 W) laser diode at the 670 nm wavelength, along with an aspheric and cylindrical lens system for shaping the beam into a collimated line of light. ADI results of biological samples (i.e. chicken breast tissue) are also presented. Image resolution is 204 &mgr;m or better in compressed chicken breast tissue approximately 3.8 mm in thickness. Digital image processing techniques are employed to improve image contrast, definition, and detectability of test structures. Because silicon is 40% reflective, scattered light at up to three times the acceptance angle is not sufficiently absorbed by the angular filter tunnels and contributes significant background noise, thus decreasing image contrast and detectability. Roughening of the tunnel surface using a NH4OH etchant solution scatters light hitting the walls, thus allowing it to be absorbed. Images after roughening show dramatic reductions in background scattered light levels between tunnels, suggesting that further experiments will make progress towards improved contrast and detectability of structures.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".