Non-invasive vascular elastography based on a new 2-D strain estimator : simulation and in vitro results
Bibliographic record
Abstract
Previous studies showed that knowledge about the composition of diseased arteries can improve diagnostic decision making. Accordingly, stiffness of the vascular wall and size ratio between structures composing it were proven to be useful to characterize pathology evolution. In order to make it as a routine clinical exam, the non-invasive vascular ultrasound elastography method (NIVE) was recently developed. With NIVE, the blood pressure induces radial strain in the vessel wall that is tracked by ultrasound means. In the current study, displacement fields were computed with a new 2D pixel-based estimator that uses pre-and post-motion radiofrequency (RF) ultrasound images. Linear affine transformations considering pixel intensity variations were used in this method. To better interpret the 2D strain distributions, Von Mises elastograms were computed. The robustness of the method was investigated with simulations of RF signals obtained from a homogeneous tissue contaminated with decorrelation noise, and from simulations of healthy and pathological vessel geometries. In addition, in vitro experiments were conducted on vascular phantoms to validate the method. Results show a good agreement between theory and estimated data. The experimental study allowed us to identify different tissue structures with specific strain distributions around the vessel lumen. These results proved the potential of our estimator to compute elastograms of pathological vessels.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| 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".