Simultaneous 6-channel optical coherence tomography using a high-power telescope-less polygon-based swept laser in dual-amplifier configuration
Bibliographic record
Abstract
Multichannel optical coherence tomography (MOCT) imaging is demonstrated using a high-power wavelength-swept laser source. The main benefit of MOCT is faster image acquisition rates without a corresponding increase in the laser tuning speed. The wavelength-swept laser was constructed using a compact telescope-less polygon-based filter in Littman arrangement. High output power, necessary for MOCT, was achieved by incorporating two serial semiconductor optical amplifiers in a ring laser cavity in Fourier domain mode-locked configuration. The laser has a measured wavelength tuning range of 111 nm centered at 1329 nm, coherence length of 5.5 mm, and total average output power of 131 mW at 43 kHz sweeping rate. Using this laser, a six-channel imaging system was constructed. The imaging arm consisted of a multi-fiber push-on connector mounted on a galvanometer-based scanner. All channels, spaced 250 μm apart, were focused at the same depth. Six-channel OCT imaging to achieve 258 kHz scan rate is demonstrated. The increase in effective frame rate using multichannel acquisition may be beneficial for 3-dimensional in-vivo imaging where bulk tissue motion can adversely affect the image quality.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".