Skip to main content

Advertisement

SpringerLink
Log in

Population-Based Bone Strain During Physical Activity: A Novel Method Demonstrated for the Human Femur

  • Original Article
  • Published:
Annals of Biomedical Engineering Aims and scope Submit manuscript

Abstract

Statistical methods are increasingly used in biomechanics for studying bone geometry, bone density distribution and function in the population. However, relating population-based bone variation to strain during activity is computationally challenging. Here, we describe a new method for calculating strain in a population, using the Superposition Principle Method Squared (SPM2), and we demonstrate the method for calculating strain in human femurs. Computed-tomography images and motion capture while walking in 21 healthy adult women were obtained earlier. Variation of femur geometry and bone distribution were modelled using active shape and appearance modelling (ASAM). Femoral strain was modelled as the weighted sum of strain generated by each force in the model plus a strain variation assumed a quadratic function of the ASAM scores. The quadratic coefficients were fitted to 35 instances drawn from the ASAM model by varying each eigenmode by ± 2 SD. The equivalent strain in matched finite-element and SPM2 calculations was obtained for 40 frames of walking for three independent cases and 50 ASAM instances. Finite-element and SPM2 solutions for walking were obtained in 44 and 3 min respectively. The SPM2 model accurately predicted strain for the three independent instances (R-squared 0.83–0.94) and the 50 ASAM instances (R-squared 0.95–1.00). The method developed enables fast and accurate calculation of population-based femoral strain.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Figure 1
Figure 2
Figure 3
Figure 4
Figure 5

Similar content being viewed by others

References

  1. Al-Dirini, R. M. A., S. Martelli, D. O’Rourke, D. Huff, J. Zhang, J. G. Clement, T. Besier, and M. Taylor. Virtual trial to evaluate the robustness of cementless femoral stems to patient and surgical variation. J. Biomech. 82:346–356, 2019.

    Article  Google Scholar 

  2. Al-Dirini, R. M. A., J. Nisyrios, M. P. Reed, and D. Thewlis. Quantifying the in vivo quasi-static response to loading of sub-dermal tissues in the human buttock using magnetic resonance imaging. Clin. Biomech. 50:70–77, 2017.

    Article  Google Scholar 

  3. Bah, M. T., J. Shi, M. Browne, Y. Suchier, F. Lefebvre, P. Young, L. King, D. G. Dunlop, and M. O. Heller. Exploring inter-subject anatomic variability using a population of patient-specific femurs and a statistical shape and intensity model. Med. Eng. Phys. 37:995–1007, 2015.

    Article  Google Scholar 

  4. Bonaretti, S., C. Seiler, C. Boichon, M. Reyes, and P. Büchler. Image-based vs. mesh-based statistical appearance models of the human femur: implications for finite element simulations. Med. Eng. Phys. 36:1626–1635, 2014.

    Article  Google Scholar 

  5. Bryan, R. Large scale, multi femur computational stress analysis using a statistical shape and intensity model. Doctoral Thesis, University of Southampton, 2010.

  6. Bryan, R., P. B. Nair, and M. Taylor. Influence of femur size and morphology on load transfer in the resurfaced femoral head: a large scale, multi-subject finite element study. J. Biomech. 45:1952–1958, 2012.

    Article  Google Scholar 

  7. Bryan, R., P. Surya Mohan, A. Hopkins, F. Galloway, M. Taylor, and P. B. Nair. Statistical modelling of the whole human femur incorporating geometric and material properties. Med. Eng. Phys. 32:57–65, 2010.

    Article  Google Scholar 

  8. Delp, S. L., F. C. Anderson, A. S. Arnold, P. Loan, A. Habib, C. T. John, E. Guendelman, and D. G. Thelen. OpenSim: open-source software to create and analyze dynamic simulations of movement. IEEE Trans. Biomed. Eng. 54:1940–1950, 2007.

    Google Scholar 

  9. Dorn, T. W., A. G. Schache, and M. G. Pandy. Muscular strategy shift in human running: dependence of running speed on hip and ankle muscle performance. J. Exp. Biol. 215:1944–1956, 2012.

    Article  Google Scholar 

  10. Faulkner, K. G., C. C. Gluer, S. Grampp, and H. K. Genant. Cross-calibration of liquid and solid QCT calibration standards: corrections to the UCSF normative data. Osteoporos. Int. 3:36–42, 1993.

    Article  CAS  Google Scholar 

  11. Grassi, L., E. Schileo, C. Boichon, M. Viceconti, and F. Taddei. Comprehensive evaluation of PCA-based finite element modelling of the human femur. Med. Eng. Phys. 36:1246–1252, 2014.

    Article  Google Scholar 

  12. Grassi, L., S. P. Väänänen, M. Ristinmaa, J. S. Jurvelin, and H. Isaksson. Prediction of femoral strength using 3D finite element models reconstructed from DXA images: validation against experiments. Biomech. Model. Mechanobiol. 16:989–1000, 2017.

    Article  Google Scholar 

  13. Hazrati Marangalou, J., K. Ito, F. Taddei, and B. van Rietbergen. Inter-individual variability of bone density and morphology distribution in the proximal femur and T12 vertebra. Bone 60:213–220, 2014.

    Article  Google Scholar 

  14. Humbert, L., R. Winzenrieth, S. Di Gregorio, T. Thomas, L. Vico, J. Malouf, and L. M. del Río Barquero. 3D analysis of cortical and trabecular bone from hip DXA: precision and trend assessment interval in postmenopausal women. J. Clin. Densitom. 22:214–218, 2019.

    Article  Google Scholar 

  15. Kersh, M. E., S. Martelli, R. M. D. Zebaze, E. Seeman, and M. G. Pandy. Mechanical loading of the femoral neck in human locomotion. J. Bone Miner. Res. 33:1999–2006, 2018.

    Article  Google Scholar 

  16. Martelli, S., D. Calvetti, E. Somersalo, and M. Viceconti. Stochastic modelling of muscle recruitment during activity. Interface Focus 5:20140094, 2015.

    Article  Google Scholar 

  17. Martelli, S., M. E. Kersh, and M. G. Pandy. Sensitivity of femoral strain calculations to anatomical scaling errors in musculoskeletal models of movement. J. Biomech. 48:3615–3624, 2015.

    Article  Google Scholar 

  18. Martelli, S., M. E. Kersh, A. G. Schache, and M. G. Pandy. Strain energy in the femoral neck during exercise. J. Biomech. 47:1784–1791, 2014.

    Article  Google Scholar 

  19. Martelli, S., H. Mokhtarzadeh, P. Pivonka, and P. R. Ebeling. The femoral neck mechanoresponse to hip extensors exercise: a case study. J. Osteoporos 2017. https://doi.org/10.1155/2017/5219541.

    Article  PubMed  PubMed Central  Google Scholar 

  20. Martelli, S., P. Pivonka, and P. R. Ebeling. Femoral shaft strains during daily activities: implications for atypical femoral fractures. Clin. Biomech. 29:869–876, 2014.

    Article  Google Scholar 

  21. Morgan, E. F., H. H. Bayraktar, and T. M. Keaveny. Trabecular bone modulus-density relationships depend on anatomic site. J. Biomech. 36:897–904, 2003.

    Article  Google Scholar 

  22. Robertson, I., and S. Sherwin. Free-surface flow simulation using hp/spectral elements. J. Comput. Phys. 155:26–53, 1999.

    Article  Google Scholar 

  23. Ruiz Wills, C., A. L. Olivares, S. Tassani, M. Ceresa, V. Zimmer, M. A. González Ballester, L. M. del Río, L. Humbert, and J. Noailly. 3D patient-specific finite element models of the proximal femur based on DXA towards the classification of fracture and non-fracture cases. Bone 121:89–99, 2019.

    Article  Google Scholar 

  24. Sarkalkan, N., H. Weinans, and A. A. Zadpoor. Statistical shape and appearance models of bones. Bone 60:129–140, 2014.

    Article  Google Scholar 

  25. 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. 41:2483–2491, 2008.

    Article  Google Scholar 

  26. Taddei, F., S. Martelli, B. Reggiani, L. Cristofolini, and M. Viceconti. Finite-element modeling of bones from CT data: sensitivity to geometry and material uncertainties. IEEE Trans. Biomed. Eng. 53:2194–2200, 2006.

    Article  Google Scholar 

  27. Väänänen, S. P., J. S. Jurvelin, and H. Isaksson. Estimation of 3D shape, internal density and mechanics of proximal femur by combining bone mineral density images with shape and density templates. Biomech. Model. Mechanobiol. 11:791–800, 2012.

    Article  Google Scholar 

  28. Viceconti, M., A. Henney, and E. Morley-Fletcher. In silico clinical trials: how computer simulation will transform the biomedical industry. Int J Clin Trials 3:37–46, 2016.

    Article  Google Scholar 

  29. Viceconti, M., P. Hunter, and R. Hose. Big data, big knowledge: big data for personalized healthcare. IEEE J. Biomed. Heal. Inform. 2015. https://doi.org/10.1109/JBHI.2015.2406883.

    Article  Google Scholar 

  30. Zhang, J., J. Hislop-Jambrich, and T. F. Besier. Predictive statistical models of baseline variations in 3-D femoral cortex morphology. Med. Eng. Phys. 38:450–457, 2016.

    Article  Google Scholar 

  31. Ziaeipoor, H., S. Martelli, M. G. Pandy, and M. Taylor. Efficacy and efficiency of multivariate linear regression for the rapid prediction of the femoral strain field during activity. Med. Eng. Phys. 63:88–92, 2019.

    Article  Google Scholar 

  32. Ziaeipoor, H., M. Taylor, M. Pandy, and S. Martelli. A novel training-free method for real-time prediction of femoral strain. J. Biomech. 86:110–116, 2019.

    Article  Google Scholar 

Download references

Acknowledgments

This work was supported by the Australian Research Council [DP180103146, FT180100338] and the Australian Government Research Training Program Scholarship (AGRTPS).

Conflict of interest

All authors declare no conflict of interest in relation to the present study.

Author information

Authors and Affiliations

  1. Medical Device Research Institute, College of Science and Engineering, Flinders University, South Rd, Tonsley, SA, 5042, Australia

    Hamed Ziaeipoor, Mark Taylor & Saulo Martelli

Authors
  1. Hamed Ziaeipoor
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mark Taylor
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Saulo Martelli
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Saulo Martelli.

Additional information

Associate Editor Umberto Morbiducci oversaw the review of this article.

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (DOC 109 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ziaeipoor, H., Taylor, M. & Martelli, S. Population-Based Bone Strain During Physical Activity: A Novel Method Demonstrated for the Human Femur. Ann Biomed Eng 48, 1694–1701 (2020). https://doi.org/10.1007/s10439-020-02483-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10439-020-02483-3

Keywords

Search

Navigation