Using a piezoelectric fiber stretcher to remove the depth ambiguity in optical Fourier domain imaging
Bibliographic record
Abstract
This paper reports the study of an Optical Fourier Domain Imaging (OFDI) setup for optical coherence tomography. One of the main drawbacks of OFDI is its inability to differentiate positive and negative depths. Some setups have already been proposed to remove this depth ambiguity by introducing a modulation by means of electro-optic or acousto-optic modulators. In our setup, we implement a piezoelectric fiber stretcher to generate a periodic phase shift between successive A-scans, thus introducing a transverse modulation. The depth ambiguity is then resolved by performing a Fourier treatment in the transverse direction before processing the data in the axial direction. It is similar to the B-M mode scanning already proposed for Spectral-Domain OCT1 but with a more efficient experimental setup. We discuss the advantages and the drawbacks of our technique compared to the technique based on acousto-optics modulators by comparing images of an onion obtained with both techniques.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".