Optical Coherence Elastography Techniques for Carotid Arterial Wall Imaging
Bibliographic record
Abstract
In this thesis, elastography is evaluated in combination with optical coherence tomography (OCT). Two approaches to OCT based elastography, Digital image correlation (DIC) and Doppler optical coherence elastography (DOCE), are evaluated for an intravascular setup using in vivo images from a porcine carotid model. DIC tracks the displacement of speckle patterns in consecutive frames, allowing the calculation of axial and lateral strain. Rapid speckle decorrelation was observed in preprocessed structural images, affecting the tracking and limiting the feasibility of this algorithm. DOCE measures axial strain based on relative tissue velocities. Rotational movement of the imaging optical fibre was the biggest source of artefacts in this imaging mode, but could be removed with a newly developed algorithm, based on the phase change induced in a surrounding catheter. The standard deviation of phase after artefact removal, measured in a stationary phantom experiment, was ~0.2 rad, corresponding to a minimum detectable velocity of 792 μm/s at a Doppler angle of 20°. The sensitivity allowed the detection of arterial blood flow velocity and pattern and the detection of adjacent veins, but did not allow direct elastography.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".