Dual-camera spectrometer with balanced detection for enhanced retinal OCT imaging in the living mouse eye
Bibliographic record
Abstract
We developed a spectral-domain optical coherence tomography (OCT) system featuring a custom dual-camera spectrometer (DCS) for balanced detection (BD) to enhance the signal-to-noise ratio in imaging. Compared to traditional single detection, the BD approach with the calibrated DCS achieves a 15.35 dB reduction in noise floor when using a supercontinuum laser. The axial resolution of the system reaches 2.9/2.2 μm in air/tissue, with a 6-dB sensitivity roll-off depth of ∼1.36 mm. In in vivo mouse retinal imaging, DCS-BD-OCT combined with temporal speckle-averaging technique can visualize highly transparent retinal ganglion cells. Additionally, for optoretinography (ORG) measurements through intensity-based approach, it successfully restored the signal dynamics obscured by noise, improving the accuracy of extracting retinal functional responses to light stimuli.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".