Optical coherence tomography detection of shear wave propagation in layered tissue equivalent phantoms
Bibliographic record
Abstract
In this work, we explored the potential of measuring shear wave propagation using Optical Coherence Elastography (OCE) in a layered phantom and based on a swept-source optical coherence tomography (OCT) system. Shear waves were generated using a piezoelectric transducer transmitting sine-wave bursts of 400 μs, synchronized with an OCT swept source wavelength sweep imaging system. The acoustic radiation force was applied to layered phantoms. The phantoms were composed of gelatin and titanium dioxide. Differential OCT phase maps, measured with and without the acoustic radiation force, demonstrate microscopic displacement generated by shear wave propagation in these phantoms of different stiffness. The OCT phase maps are acquired with a swept-source OCT (SS-OCT) system. We present a technique for calculating tissue mechanical properties by propagating shear waves in inhomogeneous tissue equivalent phantoms using the Acoustic Radiation Force (ARF) of an ultrasound transducer, and measuring the shear wave speed and its associated properties in the different layers with OCT phase maps. This method lays the foundation for future studies of mechanical property measurements of heterogeneous tissue structures, with applications in the study of aneurysms and other intravascular pathologies.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".