Refined speckle contrast estimation in OCT based on compensation of scattering-related distortions of speckle pattern parameters
Bibliographic record
Abstract
Abstract Speckle contrast (SC) parameters in optical coherence tomography (OCT) scans are formed by the interplay of several factors—local level of optical wave backscattering by the material inhomogeneities, parameters of spatial distribution of the latter and the degree of cumulative optical wave attenuation during its fourth-and-back propagation. For the optical wavelengths used in OCT, this attenuation is usually dominated by the influence of scattering in the visualized turbid tissues rather than by absorption. For sufficiently high concentrations of scatterers (at least a few scatterers in the coherence volume) characterized by comparable scattering strengths and a fairly homogeneous distribution in space, the interference of locally scattered waves should be characterized by a Rayleigh distribution of speckle amplitudes, for which the local SC tends to 0.52. Equivalently, in terms of speckle intensities, the local SC tends to unity. Local spatial inhomogeneities or strongly uneven scattering strengths lead to the appearance of increased values of SC, which may serve as a diagnostic feature of some tissue components in OCT images. At the same time, in comparison to surface speckles formed by coherent-light scattering from rough surfaces, OCT-beam attenuation during its fourth-and-back propagation may introduce intensity inhomogeneities in OCT scans even for homogeneous tissue areas. This effect results in artefactual distortion of the visible SC in comparison with the above-mentioned expected value. Moreover, the presence of individual strong scatterers may additionally non-locally distort SC values due to the appearance of elongated shadows below such scatterers, which causes lateral inhomogeneities in OCT scans. Here, we propose a refined SC parameter, which is cleaned from distorting scattering-related effects in both axial and lateral directions. Depth-resolved estimation of the optical attenuation coefficient is used to restore attenuation-free OCT scans, for which the refined SC is estimated. The efficiency of the proposed approach is demonstrated using both digital OCT phantoms with highly controlled properties and experimental OCT data.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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 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".