Adapting the Lagrangian speckle model estimator for endovascular elastography: Theory and validation with simulated radio-frequency data
Bibliographic record
Abstract
Intravascular ultrasound (IVUS) is known to be the reference tool for preoperative vessel lesion assessments and for endovascular therapy planning. Nevertheless, IVUS echograms only provide subjective information about vessel wall lesions. Since changes in the vascular tissue stiffness are characteristic of vessel pathologies, catheter-based endovascular ultrasound elastography (EVE) has been proposed in the literature as a method for outlining the elastic properties of vessel walls. In this paper, the Lagrangian Speckle Model Estimator (LSME) is formulated for investigations in EVE, i.e., using a polar coordinate system. The method was implemented through an adapted version of the Levenberg-Marquardt minimization algorithm, using the optical flow equations to compute the Jacobbian matrix. The theoretical framework was validated with simulated ultrasound rf data of mechanically complex vessel wall pathologies. The results, corroborated with Ansys finite element software, demonstrated the potential of EVE to provide useful information about the heterogeneous nature of atherosclerotic plaques.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".