Speckle Size in Optical Coherence Tomography
Bibliographic record
Abstract
Speckle is inherent to any Optical Coherence Tomography (OCT) imaging of biological tissue. It is often seen as degrading the signal, but it also carries information about the tissue microstructure. One parameter of interest is the speckle size. We study the variations in speckle size on optical phantoms with different density of scatterers. Phantoms are fabricated with a new approach by introducing silica microspheres in a curing silicon matrix, providing phantoms with a controlled density of scatterers. These phantoms are also solid, deformable, and conservable. Experimental results are obtained with Time-Domain OCT (TD-OCT). Modeling is performed by simulating a phantom as a random distribution with of discrete scatterers. Both experimental results and modeling show that the speckle size varies when there are few scatterers contained within the probed volume, the latter being defined by the coherence length and the spot size of the focusing optics. As a criterion to differentiate tissues, the speckle size has the same sensitivity as the contrast parameter that is studied in Ref. 1. This work also contributes to a better understanding of speckle in OCT.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".