Investigating Shear Wave Physics in a Generic Pediatric Left Ventricular Model via <i>In Vitro</i> Experiments and Finite Element Simulations
Bibliographic record
Abstract
Shear wave elastography (SWE) is a potentially valuable tool to noninvasively assess ventricular function in children with cardiac disorders, which could help in the early detection of abnormalities in muscle characteristics. Initial experiments demonstrated the potential of this technique in measuring ventricular stiffness; however, its performance remains to be validated as complicated shear wave (SW) propagation characteristics are expected to arise due to the complex non-homogenous structure of the myocardium. In this work, we investigated the (i) accuracy of different shear modulus estimation techniques (time-of-flight (TOF) method and phase velocity analysis) across myocardial thickness and (ii) effect of the ventricular geometry, surroundings, acoustic loading, and material viscoelasticity on SW physics. A generic pediatric (10-15-year old) left ventricular model was studied numerically and experimentally. For the SWE experiments, a polyvinylalcohol replicate of the cardiac geometry was fabricated and SW acquisitions were performed on different ventricular areas using varying probe orientations. Additionally, the phantom's stiffness was obtained via mechanical tests. The results of the SWE experiments revealed the following trends for stiffness estimation across the phantom's thickness: a slight stiffness overestimation for phase speed analysis and a clear stiffness underestimation for the TOF method for all acquisitions. The computational model provided valuable 3-D insights in the physical factors influencing SW patterns, especially the surroundings (water), interface force, and viscoelasticity. In conclusion, this paper presents a validation study of two commonly used shear modulus estimators for different ventricular locations and the essential role of SW modeling in understanding SW physics in the pediatric myocardium.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".