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Material Mapping of QCT-Derived Scapular Models: A Comparison with Micro-CT Loaded Specimens Using Digital Volume Correlation

  • Nikolas K. KnowlesEmail author
  • Jonathan Kusins
  • Mohammadreza Faieghi
  • Melissa Ryan
  • Enrico Dall’Ara
  • Louis M. Ferreira
Article

Abstract

Subject- and site-specific modeling techniques greatly improve finite element models (FEMs) derived from clinical-resolution CT data. A variety of density-modulus relationships are used in scapula FEMs, but the sensitivity to selection of relationships has yet to be experimentally evaluated. The objectives of this study were to compare quantitative-CT (QCT) derived FEMs mapped with different density-modulus relationships and material mapping strategies to experimentally loaded cadaveric scapular specimens. Six specimens were loaded within a micro-CT (33.5 μm isotropic voxels) using a custom-hexapod loading device. Digital volume correlation (DVC) was used to estimate full-field displacements by registering images in pre- and post-loaded states. Experimental loads were measured using a 6-DOF load cell. QCT-FEMs replicated the experimental setup using DVC-driven boundary conditions (BCs) and were mapped with one of fifteen density-modulus relationships using elemental or nodal material mapping strategies. Models were compared based on predicted QCT-FEM nodal reaction forces compared to experimental load cell measurements and linear regression of the full-field nodal displacements compared to the DVC full-field displacements. Comparing full-field displacements, linear regression showed slopes ranging from 0.86 to 1.06, r-squared values of 0.82–1.00, and max errors of 0.039 mm for all three Cartesian directions. Nearly identical linear regression results occurred for both elemental and nodal material mapping strategies. Comparing QCT-FEM to experimental reaction forces, errors ranged from − 46 to 965% for all specimens, with specimen-specific errors as low as 3%. This study utilized volumetric imaging combined with mechanical loading to derive full-field experimental measurements to evaluate various density-modulus relationships required for QCT-FEMs applied to whole-bone scapular loading. The results suggest that elemental and nodal material mapping strategies are both able to simultaneously replicate experimental full-field displacements and reactions forces dependent on the density-modulus relationship used.

Keywords

Finite element modeling Material mapping strategies Experimental loading Bone mechanics 

Notes

Acknowledgments

Enrico Dall’Ara and Melissa Ryan were supported by the Engineering and Physical Sciences Research Council (Grant Number: EP/P015778/1).

Supplementary material

10439_2019_2312_MOESM1_ESM.pdf (190 kb)
Supplementary material 1 (PDF 189 kb)

References

  1. 1.
    Büchler, P., N. A. Ramaniraka, L. R. Rakotomanana, J. P. Iannotti, and A. Farron. A finite element model of the shoulder: application to the comparison of normal and osteoarthritic joints. Clin. Biomech. 17:630–639, 2002.CrossRefGoogle Scholar
  2. 2.
    Burkhart, T. A., D. M. Andrews, and C. E. Dunning. Finite element modeling mesh quality, energy balance and validation methods: a review with recommendations associated with the modeling of bone tissue. J. Biomech. 46:1477–1488, 2013.CrossRefGoogle Scholar
  3. 3.
    Carter, D., and W. Hayes. The compressive behavior of bone as a two-phase porous structure. J. Bone Jt. Surg. 59(7):954–962, 1977.CrossRefGoogle Scholar
  4. 4.
    Chen, Y., E. Dall’Ara, E. Sales, K. Manda, R. Wallace, P. Pankaj, and M. Viceconti. Micro-CT based finite element models of cancellous bone predict accurately displacement once the boundary condition is well replicated: a validation study. J. Mech. Behav. Biomed. Mater. 65:644–651, 2017.CrossRefGoogle Scholar
  5. 5.
    Comini, F., M. Palanca, L. Cristofolini, E. D. Ara, and E. Dall’Ara. Uncertainties of synchrotron microCT-based digital volume correlation bone strain measurements under simulated deformation. J. Biomech. 86:232–237, 2019.CrossRefGoogle Scholar
  6. 6.
    Costa, M. C., G. Tozzi, L. Cristofolini, V. Danesi, M. Viceconti, and E. Dall’Ara. Micro finite element models of the vertebral body: validation of local displacement predictions. PLoS ONE 12:1–18, 2017.Google Scholar
  7. 7.
    Dall’Ara, E., D. Barber, and M. Viceconti. About the inevitable compromise between spatial resolution and accuracy of strain measurement for bone tissue: a 3D zero-strain study. J. Biomech. 47:2956–2963, 2014.CrossRefGoogle Scholar
  8. 8.
    Dall’Ara, E., M. Peña-Fernández, M. Palanca, M. Giorgi, L. Cristofolini, and G. Tozzi. Precision of digital volume correlation approaches for strain analysis in bone imaged with micro-computed tomography at different dimensional levels. Front. Mater. 4:31, 2017.CrossRefGoogle Scholar
  9. 9.
    Enns-Bray, W. S., H. Bahaloo, I. Fleps, O. Ariza, S. Gilchrist, R. Widmer, P. Guy, H. Pálsson, S. J. Ferguson, P. A. Cripton, and B. Helgason. Material mapping strategy to improve the predicted response of the proximal femur to a sideways fall impact. J. Mech. Behav. Biomed. Mater. 78:196–205, 2018.CrossRefGoogle Scholar
  10. 10.
    Gray, H. A., F. Taddei, A. B. Zavatsky, L. Cristofolini, and H. S. Gill. Experimental validation of a finite element model of a human cadaveric tibia. J. Biomech. Eng. 130:1–9, 2008.CrossRefGoogle Scholar
  11. 11.
    Helgason, B., S. Gilchrist, O. Ariza, P. Vogt, W. Enns-Bray, R. P. Widmer, T. Fitze, H. Pálsson, Y. Pauchard, P. Guy, S. J. Ferguson, and P. A. Cripton. The influence of the modulus-density relationship and the material mapping method on the simulated mechanical response of the proximal femur in side-ways fall loading configuration. Med. Eng. Phys. 38:679–689, 2016.CrossRefGoogle Scholar
  12. 12.
    Helgason, B., E. Perilli, E. Schileo, and F. Taddei. Mathematical relationships between bone density and mechanical properties: a literature review. Clin. Biomech. 23:135–146, 2008.CrossRefGoogle Scholar
  13. 13.
    Helgason, B., F. Taddei, H. Pálsson, E. Schileo, L. Cristofolini, M. Viceconti, and S. Brynjólfsson. A modified method for assigning material properties to FE models of bones. Med. Eng. Phys. 30:444–453, 2008.CrossRefGoogle Scholar
  14. 14.
    Hussein, A. I., D. T. Louzeiro, and E. F. Morgan. Differences in trabecular microarchitecture and simplified boundary conditions limit the accuracy of quantitative computed tomography-based finite element models of vertebral failure. J. Biomech. Eng. 140:1–11, 2018.CrossRefGoogle Scholar
  15. 15.
    Jackman, T. M., A. M. Delmonaco, and E. F. Morgan. Accuracy of finite element analyses of CT scans in predictions of vertebral failure patterns under axial compression and anterior flexion. J. Biomech. 49:1–9, 2015.Google Scholar
  16. 16.
    Keller, T. S. Predicting the compressive mechanical behavior of bone. J. Biomech. 27:1159–1168, 1994.CrossRefGoogle Scholar
  17. 17.
    Knowles, N. K., G. D. G. Langohr, G. S. Athwal, and L. M. Ferreira. Polyethylene glenoid component fixation geometry influences stability in total shoulder arthroplasty. Comput. Methods Biomech. Biomed. Engin. 22:271–279, 2018.CrossRefGoogle Scholar
  18. 18.
    Knowles, N. K., G. D. G. Langohr, M. Faieghi, A. Nelson, and L. Ferreira. Development of a validated glenoid trabecular density–modulus relationship. J. Mech. Behav. Biomed. Mater. 90:140–145, 2019.CrossRefGoogle Scholar
  19. 19.
    Knowles, N. K., G. D. G. Langohr, M. Faieghi, A. Nelson, and L. M. Ferreira. A comparison of density-modulus relationships used in finite element modeling of the shoulder. Med. Eng. Phys. 66:40–46, 2019.CrossRefGoogle Scholar
  20. 20.
    Knowles, N., J. M. Reeves, and L. M. Ferreira. Quantitative computed tomography (QCT) derived bone mineral density (BMD) in finite element studies: a review of the literature. J. Exp. Orthop. 3:36, 2016.CrossRefGoogle Scholar
  21. 21.
    Kusins, J., N. K. Knowles, M. Ryan, E. Dall’Ara, and L. M. Ferreira. Performance of QCT-derived scapula finite element models in predicting local displacements using digital volume correlation. J. Mech. Behav. Biomed. Mater. 97:339, 2019.CrossRefGoogle Scholar
  22. 22.
    Morgan, E. F., H. H. Bayraktar, and T. M. Keaveny. Trabecular bone modulus-density relationships depend on anatomic site. J. Biomech. 36(7):897–904, 2003.CrossRefGoogle Scholar
  23. 23.
    Oliviero, S., M. Giorgi, and E. D. Ara. Journal of the mechanical behavior of biomedical materials validation of finite element models of the mouse tibia using digital volume correlation. J. Mech. Behav. Biomed. Mater. 86:172–184, 2018.CrossRefGoogle Scholar
  24. 24.
    Rice, J., S. Cowin, and J. Bowman. On the dependence of the elasticity and strength of cancellous bone on apparent density. J. Biomech. 21(2):155–168, 1988.CrossRefGoogle Scholar
  25. 25.
    Schaffler, M., and D. Burr. Stiffness of compact bone: effects of porosity and density. J. Biomech. 21(1):13–16, 1988.CrossRefGoogle Scholar
  26. 26.
    Schileo, E., E. Dall’Ara, F. Taddei, A. Malandrino, T. Schotkamp, M. Baleani, and M. Viceconti. An accurate estimation of bone density improves the accuracy of subject-specific finite element models. J. Biomech. 41(11):2483, 2008.CrossRefGoogle Scholar
  27. 27.
    Schneider, C. A., W. S. Rasband, and K. W. Eliceiri. NIH image to ImageJ: 25 years of image analysis. Nat. Methods 9:671–675, 2012.CrossRefGoogle Scholar
  28. 28.
    Taddei, F., E. Schileo, B. Helgason, L. Cristofolini, and M. Viceconti. The material mapping strategy influences the accuracy of CT-based finite element models of bones: an evaluation against experimental measurements. Med. Eng. Phys. 29:973–979, 2007.CrossRefGoogle Scholar

Copyright information

© Biomedical Engineering Society 2019

Authors and Affiliations

  1. 1.School of Biomedical EngineeringThe University of Western OntarioLondonCanada
  2. 2.Department of Mechanical and Materials EngineeringThe University of Western OntarioLondonCanada
  3. 3.Roth|McFarlane Hand and Upper Limb Centre, St. Josephs Health CareLondonCanada
  4. 4.Collaborative Training Program in MSK Health Research, and Bone and Joint InstituteThe University of Western OntarioLondonCanada
  5. 5.Department of Oncology and Metabolism and INSIGNEO Institute for In Silico MedicineUniversity of SheffieldSheffieldUK
  6. 6.Roth|McFarlane Hand and Upper Limb Centre, Surgical Mechatronics Laboratory, St. Josephs Health CareLondonCanada

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