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Record W4411393885 · doi:10.1016/j.actbio.2025.06.031

Distinguishing shear and tensile myocardial wall stiffness using ex vivo anisotropic Magnetic Resonance Elastography

2025· article· en· W4411393885 on OpenAlexafffund
Cyril Tous, Guillaume Flé, Stanislas Rapacchi, Matthew McGarry, Philip V. Bayly, Keith D. Paulsen, Curtis L. Johnson, Elijah Van Houten

Bibliographic record

VenueActa Biomaterialia · 2025
Typearticle
Languageen
FieldEngineering
TopicElasticity and Material Modeling
Canadian institutionsUniversité de SherbrookeCentre Hospitalier de l’Université de Montréal
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health ResearchAlliance de recherche numérique du CanadaDeutsche ForschungsgemeinschaftCanadian Cancer SocietyNIH Clinical CenterBreast Cancer Society of Canada
KeywordsMaterials scienceAnisotropyComposite materialStiffnessIsotropyShear (geology)Magnetic resonance elastographyElastographyUltimate tensile strengthUltrasoundOpticsPhysics

Abstract

fetched live from OpenAlex

ABSTRACT The organized myofiber structure within the myocardium indicates its mechanical anisotropy. By projecting the MR Elastography (MRE) stiffness matrix along either the myocardial fiber or sheet orientations determined by Diffusion Tensor Imaging (DTI), anisotropic MRE (aMRE) maps axial and transverse shear and Young’s moduli into three tensile and six shear deformation modes. Ten healthy ex vivo swine hearts were imaged three times at 3T using MRE and DTI sequences. aMRE results showed a within-subject coefficient of variation at 19% for the fiber model and 28% for the sheet model across specimens and metrics, with coefficients lower than 15% for seven of the ten specimens across models. Repeatability coefficient of ±0.5 kPa for Young’s moduli and ±0.17 kPa for shear’s moduli, demonstrating repeatability within the 95% agreement limit. Isotropic MRE underestimated stiffnesses by 31% compared to aMRE, where anisotropic moduli aligned more closely with prior finite element studies and some mechanical loading tests. The myocardium’s anisotropic elasticity reflects with its helicoidal myofiber microstructure, with mid-wall circumferential fibers requiring twice the force to deform as longitudinal fibers at the epicardium or endocardium. At the mid-wall, fiber model values were μ ax = 1.9 ± 0.1 kPa, μ tra = 1.3 ± 0.1 kPa, E ax = 5.6 ± 0.4 kPa, and E tra = 3.8 ± 0.3 kPa. Identified deformation modes included: (FF), (NN), (FF or SS), (NN or SS), (SN or NS), (FN or FS), (SF or FS), and (SN or NF), where N is normal to both fiber (F) and sheet (S) orientations. By aligning elasticity matrices more accurately with myocardial architecture than isotropic MRE, aMRE was able to reliably measure shear and Young’s moduli in ex vivo swine hearts. These mappings of deformation modes may bring myocardial stiffness assessment closer to clinical application, providing a foundation for a non-invasive methodology capable of true mechanical characterization of the cardiac wall using MR imaging. Statement of Significance The myocardium’s anisotropic elasticity, due to its helicoidal myofiber structure, is revealed through anisotropic MR elastography, using fiber and sheet elastic models. Mid-wall circumferential fibers require twice the force to deform equally compared to epicardial or endocardial fibers. Characterizing shear and Young’s moduli across cardiac modes offers noninvasive measures of ventricular compliance, comparable to pressure-volume relationships. This could enhance early diagnosis of “stiff heart syndrome” and clarify its underlying mechanisms. Additionally, it aids understanding of myocardial pathologies, including amyloidosis, hypertrophic and dilated cardiomyopathies, and ischemic damage. By characterizing tensile and shear interactions, it may inform diagnosis and treatment of conduction issues and arrhythmia, where tissue has lost its normal mechanical behavior, while patient-specific models could optimize surgical and therapeutic outcomes.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.562
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.009
GPT teacher head0.214
Teacher spread0.205 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2025
Admission routes2
Has abstractyes

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