Improving Material Mapping in Glenohumeral Finite Element Models: A Multi-Level Evaluation
Notice bibliographique
Résumé
An improved understanding of glenohumeral bone mechanics can be elucidated using computational models derived from computed tomography data. Although computational tools, such as finite element analysis, provide a powerful quantitative technique to evaluate and answer a variety of biomechanical and clinical questions, glenohumeral finite element models (FEMs) have not kept pace with improvements in modeling techniques or model validation methods seen in other anatomic locations. The present work describes the use of multi-level computational modeling to compare, develop and validate FEMs of the glenohumeral joint.\nCommon density-modulus relationships within the literature were evaluated using a multi-level comparative testing methodology to determine if relationships from alternate anatomic locations can accurately replicate the apparent-level properties of glenoid trabecular bone. Two different relationships were able to replicate the micro-level loading to within 1.4%, compared to microFEMs when accounting for homogeneous or heterogeneous tissue moduli.\nThe multi-level comparative methodology was then used to develop a glenoid-specific trabecular density-modulus relationship. This allowed for controlled and consistent development of the relationship that was adapted for use in whole-bone scapular FEMs. The density-modulus relationship developed was able to simulate micro-level apparent loading to within 1.3%, using a QCT-density specific relationship.\nMicro-level FEM characteristics were then compared to determine the optimal parameters for microFEMs and the effect of down-sampled images as FEM input. This was accomplished by creating glenoid trabecular microFEMs from microCT images at 32 micron, 64 micron or down-sampled 64 micron, spatial resolution. It was found that microFEMs accounting for material heterogeneity at the highest spatial resolution were the most accurate. MicroFEMs generated from down-sampled images at 64 microns were found to differ from those generated from scanned 64 micron images, indicating that caution should be used with down-sampled images as input for microFEMs.\nThe optimal QCT-FEM parameters and material mapping strategies (elemental or nodal) were then explored using the same multi-level computational methodology. Little difference was found when comparing elemental or nodal material mapping strategies for all element types; however, QCT-FEMs generated with hexahedral elements and mapped with elemental material mapping, most accurately replicated micro-level apparent loading. Comparisons by material mapping strategy are also presented for linear and quadratic tetrahedral elements.\nExperimental validation of whole-bone scapular models was then explored by loading cadaveric scapulae within a microCT and using digital volume correlation (DVC) and a 6-degree of freedom load cell to compare full-field displacements and reaction loads to whole-bone scapular QCT-FEMs generated with different material mapping strategies and density-modulus relationships from the literature. It was found that elemental and nodal material mapping strategies were able to accurately replicate experimental DVC displacement field results. There was only minimal variation between elemental or nodal material mapping, and although percentage errors in reaction forces varied from -46% to 965%, QCT-FEMs mapped with density-modulus relationships from the literature were able to replicate experimental reaction loads to within 3%.\nFinally, morphometric parameters and apparent modulus between non-pathologic normal and end-stage osteoarthritic humeral trabecular bone was compared. It was found that morphometric differences compared to normal bone only occurred in the most medial aspects of end-stage OA bone, within the subchondral region. Moving distally from the articular surface showed near identical morphometric parameters. The end-stage OA group also exhibited a more linear bone-volume-modulus relationship compared to non-pathologic normal bone. The largest differences were seen at bone volume fractions greater than 0.25. This indicates that if high bone volume OA bone is being modeled, then a linear bone-volume-fraction-modulus (or density-modulus) relationship may more accurately replicate bone loading; however, if the high bone-volume-fraction bone is removed (such as with humeral joint replacement surgery), a power-law relationship similar to normal non-pathologic bone may accurately replicate bone loading.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».