Design criteria in choosing optimized OCT scanning regimes
Bibliographic record
Abstract
A comparative analysis on the performance of different scanning regimes in time domain optical coherence tomography is presented in terms of image size. Safety thresholds due to the different continuous irradiation time per transverse pixel in different scanning regimes are also considered. We present the maximum exposure level for a variety of scanning procedures, employing either A scanning (depth priority) or T scanning (transverse priority) when generating cross section images, en-face images or collecting 3D volumes. We present a comparison between such B-scan images, and different criteria to allow the user to choose the right mode of operation. Mainly, two criteria are detailed, a scanning criterion and a safety criterion. The scanning criterion depends on the number of pixels along the lateral and axial directions. The analysis shows that en-face scanning allows wider images while the longitudinal scanning is more suitable to deep cross sections. The safety criterion refers to safety levels to be observed in each scanning mode. We show that the flying spot OCT imaging has different safety limits for T- and A- based imaging modes. The analysis leads to maximum permissible optical power levels that favors T-scan imaging of wide objects. We then apply the analysis considering as object the eye.
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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 | 0.002 | 0.008 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".