Characterization of Optical Coherence Tomography Images Acquired at Large Distances With Large-Diameter Beams
Bibliographic record
Abstract
We tested the imaging capabilities for variants of a 1550-nm swept-source fiber-based optical coherence tomography system with a telecentric system incorporated at the end of its sample arm. The system was designed for in vivo imaging of burns; therefore, we acquired images from samples located at distances greater than 24 cm from the exit of the telecentric system. Each system variation had a specific combination of diameters for the reference and sample beams. In the reference arm, we used, alternately, two collimated beams with diameters of 1.5 and 14 mm, respectively. In the sample arm, we tested collimated beams with the following diameters: 1.5, 3.5, 5.7, 8.4, and 14 mm. A galvanometric mirror system scanned the collimated sample beam across the entrance pupil of the telecentric system. The sample beam exited the telecentric system parallel with its optical axis and convergent onto the sample. Depending on the collimator used in the sample arm, images were acquired with beams focused to waist diameters ranging from 40 to 240 μm. We acquired images with the sample at different locations within a ±30 mm range centered about the sample beam waist. Furthermore, we used the signal-to-noise ratio, the detected signal intensity, and the visual appearance to compare images acquired with different sample/reference beam configurations.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".