Bone and Joint Institute

Title

A comparison of density–modulus relationships used in finite element modeling of the shoulder

Document Type

Article

Publication Date

4-1-2019

Journal

Medical Engineering and Physics

Volume

66

First Page

40

Last Page

46

URL with Digital Object Identifier

10.1016/j.medengphy.2019.02.005

Abstract

© 2019 Subject- and site-specific modeling techniques greatly improve the accuracy of computational models derived from clinical-resolution quantitative computed tomography (QCT) data. The majority of shoulder finite element (FE) studies use density–modulus relationships developed for alternative anatomical locations. As such, the objectives of this study were to compare the six most commonly used density–modulus relationships in shoulder finite element (FE) studies. To achieve this, ninety-eight (98) virtual trabecular bone cores were extracted from uCT scans of scapulae from 14 cadaveric specimens (7 male; 7 female). Homogeneous tissue moduli of 20 GPa, and heterogeneous tissue moduli scaled by CT-intensity were considered. Micro finite element models (µ-FEMs) of each virtual core were compressively loaded to 0.5% apparent strain and apparent strain energy density (SED app ) was collected. Each uCT virtual core was then co-registered to clinical QCT images, QCT-FEMs created, and each of the 6 density–modulus relationships applied (6 × 98 = 588 QCT-FEMs). The loading and boundary conditions were replicated and SED app was collected and compared to µ-FEM SED app . When a homogeneous tissue modulus was considered in the µ-FEMs, SED app was best predicted in QCT-FEMs with the density–modulus relationship developed from pooled anatomical locations (QCT-FEM SED app = 0.979µ-FEM SED app + 0.0066, r 2 = 0.933). A different density–modulus relationship best predicted SED app (QCT-FEM SED app = 1.014µ-FEM SED app + 0.0034, r 2 = 0.935) when a heterogeneous tissue modulus was considered. This study compared density–modulus relationships used in shoulder FE studies using an independent computational methodology for comparing these relationships.

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