Radial shear strain elastography imaging of carotid atherosclerotic plaques in a porcine model
Bibliographic record
Abstract
The objective is to show the feasibility of shear strain elastography (SSE) in vivo with intravascular ultrasound (IVUS) radio-frequency data acquired at 20 MHz in carotid arteries of pigs. We previously proposed the Lagrangian Speckle Model Estimator (LSME) to estimate the strain tensor including the shear strain that could be involved in atherosclerotic plaque hemorrhage, inflammation, and rupture mechanisms. However, the LSME performance to compute SSE had never been validated. Atherosclerotic pigs with significant plaques on carotids were studied. To induce atherosclerosis, pigs were put on an atherogenic diet and partial ligations of common carotid arteries were performed. Diabetes was induced by selective intra-arterial injection of streptozotocin. IVUS acquisitions were performed before sacrifice and histologic analysis were realized on fixed dissected carotids. SSE maps at end diastole were estimated with a new implementation of the LSME and matching with histology sections was realized. SSE clearly identified all plaques; reported figures show SSE mapping characterized by cohabitation of high positive and high negative shear values in the specific region of the plaque. This study demonstrates the performance of the LSME implementation to estimate accurately the shear strain distribution, and the feasibility of SSE to highlight atherosclerotic plaque vulnerability characteristics.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".