Soliton microcomb based spectral domain optical coherence tomography
Bibliographic record
Abstract
Abstract Spectral domain optical coherence tomography (OCT) is a widely employed, minimally invasive bio-medical imaging technique, which requires a broadband light source, typically implemented by super-luminescent diodes. Recent advances in soliton based photonic integrated frequency combs (soliton microcombs) have enabled the development of low-noise, broadband chipscale frequency comb sources, whose potential for OCT imaging has not yet been unexplored. Here, we explore the use of dissipative Kerr soliton microcombs in spectral domain OCT and show that, by using photonic chipscale Si 3 N 4 resonators in conjunction with 1300 nm pump lasers, spectral bandwidths exceeding those of commercial OCT sources are possible. We characterized the exceptional noise properties of our source (in comparison to conventional OCT sources) and demonstrate that the soliton states in microresonators exhibit a residual intensity noise floor at high offset frequencies that is ca. 3 dB lower than a traditional OCT source at identical power, and can exhibit significantly lower noise performance for powers at the milli-Watt level. Moreover, we demonstrate that classical amplitude noise of all soliton comb teeth are correlated, i.e., common mode, in contrast to superluminescent diodes or incoherent microcomb states, which opens a new avenue to improve imaging speed and performance beyond the thermal noise limit.
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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 | 0.000 |
| 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) | 0.001 | 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".