Hybrid method of strain estimation in optical coherence elastography using combined sub‐wavelength phase measurements and supra‐pixel displacement tracking
Bibliographic record
Abstract
A novel hybrid method which combines sub‐wavelength‐scale phase measurements and pixel‐scale displacement tracking for robust strain mapping in compressional optical coherence elastography is proposed. Unlike majority of OCE methods it does not rely on initial reconstruction of displacements and does not suffer from the phase‐wrapping problem for super‐wavelength displacements. Its robustness is enabled by direct fitting of local phase gradients obviating the necessity of phase unwrapping and error‐prone numerical differentiation. Furthermore, axial displacements significantly exceeding not only the optical wavelength, but pixel scales (i.e., multiple wavelengths) can be efficiently tracked and compensated. This feature strongly reduces errors in phase‐gradient estimation and ensures high robustness with respect to both additive and decorrelation noises. Illustration of exceptionally high tolerance of the proposed method to noises: contrast of only 25% in the stiffness of the layers is clearly seen in the strain map even for equal intensities of the OCT signal and additive noise (SNR = 0 dB). magnified image Illustration of exceptionally high tolerance of the proposed method to noises: contrast of only 25% in the stiffness of the layers is clearly seen in the strain map even for equal intensities of the OCT signal and additive noise (SNR = 0 dB).
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".