An <i>in silico</i> framework to analyze the anisotropic shear wave mechanics in cardiac shear wave elastography
Bibliographic record
Abstract
Shear wave elastography (SWE) is a potential tool to non-invasively assess cardiac muscle stiffness. This study focused on the effect of the orthotropic material properties and mechanical loading on the performance of cardiac SWE, as it is known that these factors contribute to complex 3D anisotropic shear wave propagation. To investigate the specific impact of these complexities, we constructed a finite element model with an orthotropic material law subjected to different uniaxial stretches to simulate SWE in the stressed cardiac wall. Group and phase speed were analyzed in function of tissue thickness and virtual probe rotation angle. Tissue stretching increased the group and phase speed of the simulated shear wave, especially in the direction of the muscle fiber. As the model provided access to the true fiber orientation and material properties, we assessed the accuracy of two fiber orientation extraction methods based on SWE. We found a higher accuracy (but lower robustness) when extracting fiber orientations based on the location of maximal shear wave speed instead of the angle of the major axis of the ellipsoidal group speed surface. Both methods had a comparable performance for the center region of the cardiac wall, and performed less well towards the edges. Lastly, we also assessed the (theoretical) impact of pathology on shear wave physics and characterization in the model. It was found that SWE was able to detect changes in fiber orientation and material characteristics, potentially associated with cardiac pathologies such as myocardial fibrosis. Furthermore, the model showed clearly altered shear wave patterns for the fibrotic myocardium compared to the healthy myocardium, which forms an initial but promising outcome of this modeling study.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".