On the speckle size in optical coherence tomography
Bibliographic record
Abstract
Speckle is always present in Optical Coherence Tomography (OCT) measurements. To a first approximation, the speckle size is determined by the OCT resolution length and the point spread function of the focusing optics in the sample arm. But the speckle size is also affected by the tissue microstructure. We demonstrate this phenomena by performing measurements on optical phantoms with a controlled density of scatterers using time-domain OCT. In the very low density limit, the scatterers are easily identified on the OCT cross-section and, in fact, one can hardly speak of a speckle pattern. The corresponding speckle size is the resolution length axially and the point spread function of the focusing optics transversally. As the number of scatterers increases, a true speckle field appears and the measured speckle size decreases. In the high density limit, the speckle size reaches an asymptotic value that is about 70% of its low-density regime values. In addition to experimental results, theoretical estimates of the limiting speckle size values are presented. Our work contributes to a better understanding of speckle in optical coherence tomography.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.004 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".