Identification of hyperelastic properties of CMR patient-specific left ventricle by finite elements and virtual fields method
Bibliographic record
Abstract
Abstract Detection of left ventricle (LV) myocardial dysfunction after doxorubicin-based chemotherapy is investigated by determining myocardial stiffness, which is a potential clinical biomarker for the monitoring of heart failure (HF). The combination of cardiac magnetic resonance (CMR) imaging and the finite element method (FEM) was used to estimate anisotropic elastic stiffness in the LV. The myocardium also has a complex geometry with nonlinear hyperelastic properties leading to large deformation. Within the proposed framework, which generates the LV mesh and reconstructs the strain field from the existing CMR data, we apply the virtual field method (VFM) to determine the hyperelastic material parameters. Minimizing an energy-based objective function obtained from VFM identifies the unknown parameters coupled in nonlinear constitutive law considering passive myocardial behavior. While Full-field characterization using VFM is valuable for studying regular-shaped models, we propose applying this method when particularly looking into ventricular remodeling caused by doxorubicin, in the context of cardiotoxicity. In the cardiac diastolic phase, the estimated stiffness of VFM results with FEM validation is compared for a case study of leukemia cancer survivors separated into three groups.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".