Characterization of Atherosclerotic Plaques and Mural Thrombi With Intravascular Ultrasound Elastography: A Potential Method Evaluated in an Aortic Rabbit Model and a Human Coronary Artery
Bibliographic record
Abstract
Plaque rupture is correlated with the plaque morphology, composition, mechanical properties, and with the blood pressure. Whereas the geometry can accurately be assessed with intravascular ultrasound (IVUS) imaging, intravascular elastography (IVE) is capable of extracting information on the plaque local mechanical properties and composition. This paper reports additional IVE validation data regarding reproducibility and potential to characterize atherosclerotic plaques and mural thrombi. In a first investigation, radio frequency (RF) data were acquired from the abdominal aorta of an atherosclerotic rabbit model. In a second investigation, IVUS RF data were recorded from the left coronary artery of a patient referred for angioplasty. In both cases, Galaxy IVUS scanners (Boston Scientific, Freemont, CA), equipped with 40 MHz Atlantis catheters, were used. Elastograms were computed using two methods, the Lagrangian speckle model estimator (LSME) and the scaling factor estimator (SFE). Corroborated with histology, the LSME and the SFE both clearly detected a soft thrombus attached to the vascular wall. Moreover, shear elastograms, only available with the LSME, confirmed the presence of the thrombus. Additionally, IVE was found reproducible with consistent elastograms between cardiac cycles (CCs). Regarding the human dataset, only the LSME was capable of identifying a plaque that presumably sheltered a lipid core. Whereas such an assumption could not be certified with histology, radial shear and tangential strain LSME elastograms enabled the same conclusion. It is worth emphasizing that this paper reports the first ever in vivo tangential strain elastogram with regards to vascular imaging, due to the LSME. It is concluded that the IVE was reproducible exhibiting consistent strain patterns between CCs. The IVE might provide a unique tool to assess coronary wall lesions.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".