Improvement of an intravascular ultrasound elasticity modulus imaging approach for detecting vulnerable atherosclerotic plaques
Bibliographic record
Abstract
Atherosclerotic plaque rupture is the major cause of acute coronary syndrome, myocardial infarction, and stroke in the western world. Stress concentration is recognized to be a good indicator of vulnerable plaques (VP). The Lagrangian speckle model estimator (LSME) for vascular ultrasound elastography, developed by our group, provides the strain field within the plaque. However, evaluation of the stress field relies on a precise identification of the mechanical properties of plaque components. As a response to this need, our group recently developed an approach called imaging modulography (iMOD). iMOD uses a continuum-mechanics-based segmentation method and the inverse finite-element method to reconstruct elasticity maps (or modulograms) of atheroma plaques based on the radial strain field calculated by the LSME. The present theoretical study was designed to further develop segmentation and optimization procedures of iMOD to incorporate both radial and shear components of the strain tensor. Simulated IVUS images of coronary lesions with known material properties and known stress fields were used to validate the new iMOD algorithm and assess its robustness and performance in detection and quantification of VPs. The results demonstrate promising benefits of the new optimized iMOD-LSME clinical imaging method for VP detection.
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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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".