Angular domain imaging of objects within highly scattering media using silicon micromachined collimating arrays
Bibliographic record
Abstract
Optical imaging of objects within highly scattering media, such as tissue, requires the detection of ballistic/quasi-ballistic photons through these media. Recent works have used phase/coherence domain or time domain tomography (femtosecond laser pulses) to detect the shortest path photons through scattering media. This work explores an alternative, angular domain imaging, which uses collimation detection capabilities of small acceptance angle devices to extract photons emitted aligned closely to a laser source. It employs a high aspect ratio, micromachined collimating detector array fabricated by high-resolution silicon surface micromachining. Consider a linear collimating array of very high aspect ratio (200: 1) containing 51×1000 μm etched channels with 102-μm spacing over a 10-mm silicon width. With precise array alignment to a laser source, unscattered light passes directly through the channels to the charge coupled device detector and the channel walls absorb the scattered light at angles >0.29°. Objects within a scattering medium were scanned quickly with a computer-controlled Z axis table. High-resolution images of 100-μm-wide lines and spaces were detected at scattered-to-ballistic ratios of 5×105: 1, with objects located near the middle of the sample seen at even higher levels. At >5×106: 1 ratios, a uniform background of scattered illumination degrades the image contrast unless recovered by background subtraction. Monte Carlo simulation programs designed to test the angular domain imaging concept showed that the collimator detects the shortest path length photons, as in other optical tomography methods. Furthermore, the collimator acts as an optical filter to remove scattered light while preserving the image resolution. Simulations suggest smaller channels and longer arrays could enhance detection by >100.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".