Developing broadband sources for optical coherence tomography
Bibliographic record
Abstract
Optical coherence tomography (OCT) is an emerging medical diagnostic technology for noninvasive in situ and in vivo cross-sectional morphological imaging of transparent or nontransparent biological tissues and materials on a micrometer scale. The technique uses low coherence interferometry to extract the intensity of the reflected signal as a function of penetration depth in the sample and is analogous to ultrasound except that much shorter wavelength infrared radiation is used rather than sound waves. Among the key enabling technologies for OCT systems are high-power, broadband optical sources (BBS). Such sources are required to provide large dynamic range and sensitivity, as well as very high axial resolution. In this paper, we present our ongoing work on developing BBS based on the amplified spontaneous emission (ASE) from semiconductor optical amplifiers (SOAs) and erbium-doped fiber amplifiers (EDFAs). We target sources spanning the S, C, and L bands, with milliwatts of output power and smoothly shaped output spectra. In terms of shaping the output spectra, we consider different designs of gain flattening filters based on side-tapped fiber Bragg gratings (FBGs) as well as specially apodized FBGs operating in transmission. In terms of the source development, we have developed strained multiple-quantum well SOAs and hybrid SOA-EDFA structures. In the hybrid structures, we have also investigated the possibility of exploiting the unused ASE from the SOA as a secondary input to the L-band EDFA. We have also explored techniques such as double-passing to enhance efficiency as well as gain-clamping to provide some inherent spectral flattening.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".