Skip to main content
SpringerLink
Log in

Finite Element-Based Mechanical Assessment of Bone Quality on the Basis of In Vivo Images

  • Imaging (T Lang and F Wehrli, Section Editors)
  • Published:
Current Osteoporosis Reports Aims and scope Submit manuscript

Abstract

Beyond bone mineral density (BMD), bone quality designates the mechanical integrity of bone tissue. In vivo images based on X-ray attenuation, such as CT reconstructions, provide size, shape, and local BMD distribution and may be exploited as input for finite element analysis (FEA) to assess bone fragility. Further key input parameters of FEA are the material properties of bone tissue. This review discusses the main determinants of bone mechanical properties and emphasizes the added value, as well as the important assumptions underlying finite element analysis. Bone tissue is a sophisticated, multiscale composite material that undergoes remodeling but exhibits a rather narrow band of tissue mineralization. Mechanically, bone tissue behaves elastically under physiologic loads and yields by cracking beyond critical strain levels. Through adequate cell-orchestrated modeling, trabecular bone tunes its mechanical properties by volume fraction and fabric. With proper calibration, these mechanical properties may be incorporated in quantitative CT-based finite element analysis that has been validated extensively with ex vivo experiments and has been applied increasingly in clinical trials to assess treatment efficacy against osteoporosis.

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

Fig. 1

Similar content being viewed by others

References

Papers of particular interest, published recently, have been highlighted as: • Of importance

  1. Reisinger AG, Pahr DH, Zysset PK. Sensitivity analysis and parametric study of elastic properties of an unidirectional mineralized bone fibril-array using mean field methods. Biomech Model Mechanobiol. 2010;9(5):499–510.

  2. Gebhardt W. Ueber funktionell wichtige anordnungsweisen der groeberen und feineren bauelemente des wirbeltierknochens. Archiv fuer Entwicklungsmechanik der Organismen, 1906; 20:187–322.

  3. Frost HL. Presence of microcracks in vivo in bone. Henry Ford Hosp Med Bull. 1960;8:25–35.

    Google Scholar 

  4. Knothe-Tate ML. Whither flows the fluid in bone? An osteocyte’s perspective. J Biomech. 2003;36(10):1409–24.

    Article  PubMed  Google Scholar 

  5. Amprino R, Engstroem A. Studies on X-ray absorption and diffraction of bone tissue. Acta Anat. 1952;15(1):1–22.

    Article  CAS  PubMed  Google Scholar 

  6. Boivin G, Baud C-A. Microradiographic methods for calcified tissues. In: Dickson GR, editor. Methods of calcified tissue preparation. Amsterdam: Elsevier; 1984. p. 391–412.

    Google Scholar 

  7. Fratzl P, Fratzl-Zelman N, Klausdorfer K, Vogl G, Koller K. Nucleation and growth of mineral crystals in bone studied by small-angle X-ray scattering. Calcif Tissue Int. 1991;48:407–13.

    Article  CAS  PubMed  Google Scholar 

  8. Gupta HS, Seto J, Wagermaier W, Zaslansky P, Boesecke P, Fratzl P. Cooperative deformation of mineral and collagen in bone at the nanoscale. Proc Natl Acad Sci U S A. 2006;103(47):17741–6.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Peyrin F, Salome M, Nuzzo S, Cloetens P, Laval-Jeantet A-M, Baruchel J. Perspectives in three-dimensional analysis of bone samples using synchrotron radiation. Cell Mol Biol. 2000;46(6):1089–102.

    CAS  PubMed  Google Scholar 

  10. Hesse B, Varga P, Langer M, Pacureanu A, Schrof S, Mannicke N, et al. Canalicular network morphology is the major determinant of the spatial distribution of mass density in human bone tissue: evidence by means of synchrotron radiation phase-contrast nano-CT. J Bone Miner Res. 2015;30(2):346–56.

    Article  CAS  PubMed  Google Scholar 

  11. Roschger P, Fratzl P, Eschberger J, Klaushofer K. Validation of quantitative backscattered electron imaging for the measurement of mineral density distribution in human biopsies. Bone. 1998;23(4):319–26.

    Article  CAS  PubMed  Google Scholar 

  12. Landis WJ, Hodgens KJ, Arena J, Song MJ, McEwen BF. Structural relations between collagen and mineral in bone as determined by high voltage electron microscopic tomography. Microsc Res Tech. 1996;33:192–202.

    Article  CAS  PubMed  Google Scholar 

  13. Reznikov N, Shahar R, Weiner S. Bone hierarchical structure in three dimensions. Acta Biomater. 2014;10(9):3815–26.

    Article  PubMed  Google Scholar 

  14. Paschalis EP, Verdelis K, Doty SB, Boskey AL, Mendelsohn R, Yamauchi M. Spectroscopic characterization of collagen cross-links in bone. J Bone Miner Res. 2001;16(10):1821–8.

    Article  CAS  PubMed  Google Scholar 

  15. Kazanci M, Roschger P, Paschalis EP, Klaushofer K, Fratzl P. Bone osteonal tissues by Raman spectral mapping: orientation-composition. J Struct Biol. 2006;156(3):489–96.

    Article  CAS  PubMed  Google Scholar 

  16. Unal M, Yang S, Akkus O. Molecular spectroscopic identification of the water compartments in bone. Bone. 2014;67:228–36.

    Article  CAS  PubMed  Google Scholar 

  17. Ong HH, Wright AC, Wehrli FW. Deuterium nuclear magnetic resonance unambiguously quantifies pore and collagen-bound water in cortical bone. J Bone Miner Res. 2012;27(12):2573–81.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Li C, Seifert AC, Rad HS, Bhagat YA, Rajapakse CS, Sun W, et al. Cortical bone water concentration: dependence of MR imaging measures on age and pore volume fraction. Radiology. 2014;272(3):796–806.

    Article  PubMed  PubMed Central  Google Scholar 

  19. Wang X, Shen X, Li X, Agrawal CM. Age-related changes in the collagen network and toughness of bone. Bone. 2002;31(1):1–7.

    Article  PubMed  Google Scholar 

  20. Fantner GE, Adams J, Turner P, Thurner PJ, Fisher LW, Hansma PK. Nanoscale ion mediated networks in bone: osteopontin can repeatedly dissipate large amounts of energy. Nano Lett. 2007;7(8):2491–8.

    Article  CAS  PubMed  Google Scholar 

  21. Bouxsein ML, Karasik D. Bone geometry and skeletal fragility. Curr Osteoporos Rep. 2006;4(2):49–56.

    Article  PubMed  Google Scholar 

  22. Roschger P, Gupta HS, Berzlanovich A, Ittner G, Dempster DW, Fratzl P, et al. Constant mineralization density distribution in cancellous human bone. Bone. 2003;32:316–23.

    Article  CAS  PubMed  Google Scholar 

  23. Gong JK, Arnold JS, Cohn H. Composition of trabecular and cortical bone. Anat Rec. 1964;149:325–32.

    Article  CAS  PubMed  Google Scholar 

  24. Dall’Ara E, Varga P, Pahr D, Zysset P. A calibration methodology of QCT BMD for human vertebral body with registered micro-CT images. Med Phys. 2011;38:2602–8.

    Article  PubMed  Google Scholar 

  25. Turner CH, Burr DB. Basic biomechanical measurements of bone: a tutorial. Bone. 1993;14:595–608.

    Article  CAS  PubMed  Google Scholar 

  26. Martin RB, Ishida J. The relative effects of collagen fiber orientation, porosity, density, and mineralization of bone strength. J Biomech. 1989;22(5):419–26.

    Article  CAS  PubMed  Google Scholar 

  27. McCalden RW, Mcgeough JA, Barker MB, Court-Brown C. Age-related changes in the tensile properties of cortical bone. J Bone Joint Surg. 1993;75-A(8):1193–205.

    Article  Google Scholar 

  28. Zioupos P. Ageing human bone: factors affecting its biomechanical properties and the role of collagen. J Biomater Appl. 2001;15:187–229.

    Article  CAS  PubMed  Google Scholar 

  29. Schaer MB, Choi K, Milgrom C. Aging and matrix microdamage accumulation in human compact bone. Bone. 1995;17(6):521–5.

    Article  Google Scholar 

  30. Carter DR, Hayes WC. Compact bone fatigue damage—I. Residual strength and stiffness. J Biomech. 1977;10:325–37.

    Article  CAS  PubMed  Google Scholar 

  31. Agcaoglu S, Akkus O. Acoustic emission based monitoring of the microdamage evolution during fatigue of human cortical bone. J Biomech Eng. 2013;135(8):81005.

    Article  PubMed  Google Scholar 

  32. Haupert S, Guerard S, Peyrin F, Mitton D, Laugier P. Non destructive characterization of cortical bone micro-damage by nonlinear resonant ultrasound spectroscopy. PLoS One. 2014;9(1):e83599.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  33. Granke M, Does MD, Nyman JS. The role of water compartments in the material properties of cortical bone. Calcif Tissue Int. 2015;97:292–307.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Zimmermann EA, Gludovatz B, Schaible E, Busse B, Ritchie RO. Fracture resistance of human cortical bone across multiple length-scales at physiological strain rates. Biomaterials. 2014;35(21):5472–81.

    Article  CAS  PubMed  Google Scholar 

  35. Bridges D, Randall C, Hansma PK. A new device for performing reference point indentation without a reference probe. Rev Sci Instrum. 2012;83(4):044301.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  36. Diez-Perez A, Guerri R, Nogues X, Caceres E, Pena MJ, Mellibovsky L, et al. Microindentation for in vivo measurement of bone tissue mechanical properties in humans. J Bone Miner Res. 2010;25(8):1877–85.

    Article  PubMed  PubMed Central  Google Scholar 

  37. Dempster DW, Compston JE, Drezner MK, Glorieux FH, Kanis JA, Malluche H, et al. Standardized nomenclature, symbols, and units for bone histomorphometry: a 2012 update of the report of the ASBMR Histomorphometry Nomenclature Committee. J Bone Miner Res. 2013;28(1):2–17.

    Article  PubMed  PubMed Central  Google Scholar 

  38. Hildebrand T, Laib A, Mller R, Dequeker J, Regsegger P. Direct three dimensional morphometric analysis of human cancellous bone: microstructural data from spine, femur, iliac crest and calcaneus. J Bone Miner Res. 1999;14(7):1167–74.

    Article  CAS  PubMed  Google Scholar 

  39. Hahn M, Vogel M, Pompesius-Kempa M, Delling G. Trabecular bone pattern factor—a new parameter for simple quantification of bone microarchitecture. Bone. 1992;13(4):327–30.

    Article  CAS  PubMed  Google Scholar 

  40. Odgaard A, Gundersen HJG. Quantification of connectivity in cancellous bone, with special emphasis on 3-D reconstructions. Bone. 1993;14:173–82.

    Article  CAS  PubMed  Google Scholar 

  41. Hildebrand T, Ruegsegger P. Quantification of bone microarchitecture with the structure model index. Comput Methods Biomech Biomed Engin. 1997;1:15–23.

    Article  PubMed  Google Scholar 

  42. Benhamou CL, Lespessailles E, Jacquet G. Architecture osseuse trabculaire: caractrisation par une mthode d’analyse fractale. Expansion Scientifique Franaise. 1994;61(5):297–300.

    CAS  Google Scholar 

  43. Stauber M, Muller R. Volumetric spatial decomposition of trabecular bone into rods and plates—a new method for local bone morphometry. Bone. 2006;38(4):475–84.

    Article  PubMed  Google Scholar 

  44. Liu XS, Sajda P, Saha PK, Wehrli FW, Bevill G, Keaveny TM, et al. Complete volumetric decomposition of individual trabecular plates and rods and its morphological correlations with anisotropic elastic moduli in human trabecular bone. J Bone Miner Res. 2008;23(2):223–35.

    Article  PubMed  Google Scholar 

  45. Whitehouse WJ. The quantitative morphology of anisotropic trabecular bone. J Microsc. 1974;101:153–68.

    Article  CAS  PubMed  Google Scholar 

  46. Harrigan TP, Mann RW. Characterization of microstructural anisotropy in orthotropic materials using a second rank tensor. J Mater Sci. 1984;19:761–7.

    Article  CAS  Google Scholar 

  47. Odgaard A. Three-dimensional methods for quantification of cancellous bone architecture. Bone. 1997;20(4):315–28.

    Article  CAS  PubMed  Google Scholar 

  48. Rotter M, Berg A, Langenberger H, Grampp S, Imhof H, Moser E. Autocorrelation analysis of bone structure. J Magn Reson Imaging. 2001;14(1):87–93.

    Article  CAS  PubMed  Google Scholar 

  49. Saha PK, Wehrli FW. A robust method for measuring trabecular bone orientation anisotropy at in vivo resolution using tensor scale. Pattern Recogn. 2004;37(9):1935–44.

    Article  Google Scholar 

  50. Tabor Z, Rokita E. Quantifying anisotropy of trabecular bone from gray-level images. Bone. 2007;40(4):966–72.

    Article  PubMed  Google Scholar 

  51. Varga P, Zysset PK. Sampling sphere orientation distribution: an efficient method to quantify trabecular bone fabric on grayscale images. Med Image Anal. 2009;13(3):530–41.

    Article  CAS  PubMed  Google Scholar 

  52. Lenaerts L, Wirth AJ, van Lenthe GH. Quantification of trabecular spatial orientation from low-resolution images. Comput Methods Biomech Biomed Engin. 2015;18(13):1392–9.

    Article  CAS  PubMed  Google Scholar 

  53. Doube M, Klosowski MM, Arganda-Carreras I, Cordelieres FP, Dougherty RP, Jackson JS, et al. BoneJ: free and extensible bone image analysis in ImageJ. Bone. 2010;47(6):1076–9.

    Article  PubMed  PubMed Central  Google Scholar 

  54. Rincon-Kohli L, Zysset PK. Multi-axial mechanical properties of human trabecular bone. Biomech Model Mechanobiol. 2009;8(3):195–208.

    Article  PubMed  Google Scholar 

  55. Charlebois M, Pretterklieber M, Zysset PK. The role of fabric in the large strain compressive behavior of human trabecular bone. J Biomech Eng. 2010;132(12):121006.

    Article  PubMed  Google Scholar 

  56. Thurner PJ. Atomic force microscopy and indentation force measurement of bone. Wiley Interdiscip Rev Nanomed Nanobiotechnol. 2009;1(6):624–49.

    Article  PubMed  Google Scholar 

  57. Raum K, Leguerney I, Chandelier F, Talmant M, Saied A, Peyrin F, et al. Site-matched assessment of structural and tissue properties of cortical bone using scanning acoustic microscopy and synchrotron radiation μCT. Phys Med Biol. 2006;51(3):733–46.

    Article  CAS  PubMed  Google Scholar 

  58. Spiesz EM, Reisinger AG, Kaminsky W, Roschger P, Pahr DH, Zysset PK. Computational and experimental methodology for site-matched investigations of the influence of mineral mass fraction and collagen orientation on the axial indentation modulus of lamellar bone. J Mech Behav Biomed Mater. 2013;28:195–205.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Luczynski KW, Steiger-Thirsfeld A, Bernardi J, Eberhardsteiner J, Hellmich C. Extracellular bone matrix exhibits hardening elastoplasticity and more than double cortical strength: evidence from homogeneous compression of non-tapered single micron-sized pillars welded to a rigid substrate. J Mech Behav Biomed Mater. 2015;52:51–62.

    Article  PubMed  Google Scholar 

  60. Jimenez-Palomar I, Shipov A, Shahar R, Barber AH. Influence of SEM vacuum on bone micromechanics using in situ AFM. J Mech Behav Biomed Mater. 2012;5(1):149–55.

    Article  PubMed  Google Scholar 

  61. Schwiedrzik J, Raghavan R, Burki A, Lenader V, Wolfram U, Michler J, et al. In situ micropillar compression reveals superior strength and ductility but an absence of damage in lamellar bone. Nat Mater. 2014;13(7):740–7.

    Article  CAS  PubMed  Google Scholar 

  62. Fritsch A, Hellmich C. Universal microstructural patterns in cortical and trabecular, extracellular and extravacular bone materials: micromechanics-based prediction of anisotropic elasticity. J Theor Biol. 2007;244:597–620.

    Article  CAS  PubMed  Google Scholar 

  63. Predoi-Racila M, Crolet JM. Human cortical bone: the SiNuPrOs model. Comput Methods Biomech Biomed Engin. 2008;11(2):169–87.

    Article  CAS  PubMed  Google Scholar 

  64. Genant HK, Jiang YB. Perspectives on advances in bone imaging for osteoporosis. In: Qin L, Genant HK, Griffth JF, Leung KS, editors. Advanced bioimaging technologies in assessment of the quality of bone and scaffold materials. Berlin: Springer; 2007. p. 5–26.

    Chapter  Google Scholar 

  65. Adams JE. Advances in bone imaging for osteoporosis. Nat Rev Endocrinol. 2013;9(1):28–42.

    Article  CAS  PubMed  Google Scholar 

  66. Brandi ML. Microarchitecture, the key to bone quality. Rheumatology (Oxford). 2009;48 Suppl 4:3–8.

    Article  Google Scholar 

  67. Mulder L, van Rietbergen B, Noordhoek NJ, Ito K. Determination of vertebral and femoral trabecular morphology and stiffness using a flat-panel C-arm-based CT approach. Bone. 2012;50:200–8.

    Article  PubMed  Google Scholar 

  68. Klintstrom E, Smedby O, Moreno R, Brismar TB. Trabecular bone structure parameters from 3D image processing of clinical multi-slice and conebeam computed tomography data. Skeletal Radiol. 2014;43(2):197–204.

    Article  PubMed  Google Scholar 

  69. Pahr DH, Schwiedrzik J, Dall’Ara E, Zysset PK. Clinical versus pre-clinical FE models for vertebral body strength predictions. J Mech Behav Biomed Mater. 2013;33:76–83.

    Article  Google Scholar 

  70. Luisier B, Dallara E, Pahr DH. Orthotropic HR-pQCT-based FE models improve strength predictions for stance but not for side-way fall loading compared to isotropic QCT-based FE models of human femurs. J Mech Behav Biomed Mater. 2014;32C:287–99.

    Article  Google Scholar 

  71. Zysset P, Pahr D, Engelke K, Genant HK, Mc-Clung MR, Kendler DL, et al. Comparison of proximal femur and vertebral body strength improvements in the FREEDOM trial using an alternative finite element methodology. Bone. 2015;81:122–30.

    Article  PubMed  Google Scholar 

  72. Kang Y, Engelke K, Kalender WA. A new accurate and precise 3-D segmentation method for skeletal structures in volumetric CT data. Med Imaging, IEEE Transactions. 2003;22(5):586–98.

    Article  Google Scholar 

  73. Heimann T, Meinzer H-P. Statistical shape models for 3D medical image segmentation: a review. Med Image Anal. 2009;13(4):543–63.

    Article  PubMed  Google Scholar 

  74. Pahr DH, Zysset PK. From high-resolution CT data to finite element models: development of an integrated modular framework. Comput Methods Biomech Biomed Engin. 2009;12:45–57.

    Article  PubMed  Google Scholar 

  75. Treece GM, Gee AH, Mayhew PM, Poole KES. High resolution cortical bone thickness measurement from clinical CT data. Med Image Anal. 2010;14(3):276–90.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  76. Ridler TW, Calvard S. Picture thresholding using an iterative selection method. IEEE T Syst Man Cyb. 1978;8(8):630–2.

    Article  Google Scholar 

  77. Laib A, Häuselmann HJ, Rüegsegger P. In vivo high resolution 3D-QCT of the human forearm. Technol Health Care. 1998;6(5–6):329–37.

    CAS  PubMed  Google Scholar 

  78. Scherf H, Tilgner R. A new high-resolution computed tomography (CT) segmentation method for trabecular bone architectural analysis. Am J Phys Anthropol. 2009;140(1):39–51.

    Article  PubMed  Google Scholar 

  79. Parkinson IH, Badiei A, Fazzalari NL. Variation in segmentation of bone from micro-CT imaging: implications for quantitative morphometric analysis. Australas Phys Eng Sci Med. 2008;31(2):160–4.

    Article  CAS  PubMed  Google Scholar 

  80. Viceconti M, Zannoni C, Testi D, Cappello A. CT data sets surface extraction for biomechanical modeling of long bones. Comput Methods Programs Biomed. 1999;9(3):159–66.

    Article  Google Scholar 

  81. Rathnayaka K, Sahama T, Schuetz MA, Schmutz B. Effects of CT image segmentation methods on the accuracy of long bone 3D reconstructions. Med Eng Phys. 2011;33(2):226–33.

    Article  PubMed  Google Scholar 

  82. Homminga J, van Rietbergen B, Lochmuller EM, Weinans H, Eckstein F, Huiskes R. The osteoporotic vertebral structure is well adapted to the loads of daily life, but not to infrequent “error” loads. Bone. 2004;34(3):510–6.

    Article  CAS  PubMed  Google Scholar 

  83. Verhulp E, van Rietbergen B, Huiskes R. Comparison of micro-level and continuum-level voxel models of the proximal femur. J Biomech. 2006;39(16):2951–7.

    Article  CAS  PubMed  Google Scholar 

  84. Fields AJ, Eswaran SK, Jekir MG, Keaveny TM. Role of trabecular microarchitecture in whole-vertebral body biomechanical behavior. J Bone Miner Res. 2009;24(9):1523–30.

    Article  PubMed  PubMed Central  Google Scholar 

  85. Nawathe S, Akhlaghpour H, Bouxsein ML, Keaveny TM. Microstructural failure mechanisms in the human proximal femur for sideways fall loading. J Bone Miner Res. 2013;29:507–15.

    Article  Google Scholar 

  86. Faulkner KG, Cann CE, Hasegawa BH. Effect of bone distribution on vertebral strength: assessment with patient-specific nonlinear finite element analysis. Radiology. 1991;179(3):669–74.

    Article  CAS  PubMed  Google Scholar 

  87. Martin H, Werner J, Andresen R, Schober HC, Schmitz KP. Noninvasive assessment of stiffness and failure load of human vertebrae from CT-data. Biomed Tech (Berl). 1998;43(4):82–8.

    Article  CAS  Google Scholar 

  88. Crawford RP, Cann CE, Keaveny TM. Finite element models predict in vitro vertebral body compressive strength better than quantitative computed tomography. Bone. 2003;33(4):744–50.

    Article  PubMed  Google Scholar 

  89. Imai K, Ohnishi I, Bessho M, Nakamura K. Nonlinear finite element model predicts vertebral bone strength and fracture site. Spine. 2006;31:1789–94.

    Article  PubMed  Google Scholar 

  90. Schileo E, Taddei F, Malandrino A, Cristofolini L, Viceconti M. Subject-specific finite element models can accurately predict strain levels in long bones. J Biomech. 2007;40(13):2982–9.

    Article  PubMed  Google Scholar 

  91. Pahr DH, Zysset PK. A comparison of enhanced continuum FE with micro FE models of human vertebral bodies. J Biomech. 2009;42:455–62.

    Article  PubMed  Google Scholar 

  92. van Rietbergen B, Weinans H, Huiskes R, Odgaard A. A new method to determine trabecular bone elastic properties and loading using micromechanical finite element models. J Biomech. 1995;28(1):69–81.

    Article  PubMed  Google Scholar 

  93. Adams MF, Bayraktar HH, Keaveny TM, Papadopoulos P. Ultrascalable implicit finite element analyses in solid mechanics with over a half a billion degrees of freedom. In Proceedings of the 2004 ACM/IEEE conference on supercomputing, page 34. IEEE Computer Society, 2004.

  94. Arbenz P, van Lenthe GH, Mennel U, Müller R, Sala M. A scalable multi-level preconditioner for matrix-free μ-finite element analysis of human bone structures. Int J Numer Methods Eng. 2008;73(7):927–47.

    Article  Google Scholar 

  95. Flaig C, Arbenz P. A highly scalable matrix-free multigrid solver for FE analysis based on a pointer-less octree. In Lirkov I, Margenov S, Waniewski J, editors. Large-scale scientific computing, volume 7116 of Lecture Notes in Computer Science, p. 498–506. Berlin: Springer, 2012.

  96. Homminga J, Weinans H, Gowin W, Felsenberg R, Huiskes D. Osteoporosis changes the amount of vertebral trabecular bone at risk of fracture but not the vertebral load distribution. Spine. 2001;26:1555–60.

    Article  CAS  PubMed  Google Scholar 

  97. Chevalier Y, Pahr DH, Charlebois M, Heini P, Schneider E, Zysset PK. Cement distribution, volume, and compliance in vertebroplasty: some answers from an anatomy-based nonlinear finite element study. Spine. 2008;33:1722–30.

    Article  PubMed  Google Scholar 

  98. Viceconti M, Bellingeri L, Cristofolini L, Toni A. A comparative study on different methods of automatic mesh generation of human femurs. Med Eng Phys. 1998;20(1):1–10.

    Article  CAS  PubMed  Google Scholar 

  99. Treece GM, Prager RW, Gee AH. Regularised marching tetrahedra: improved iso-surface extraction. Comput Graph. 1999;23(4):583–98.

    Article  Google Scholar 

  100. Ito Y, Shum PC, Shih AM, Soni BK, Nakahashi K. Robust generation of high-quality unstructured meshes on realistic biomedical geometry. Int J Numer Methods Eng. 2006;65(6):943–73.

    Article  Google Scholar 

  101. Taddei F, Schileo E, Helgason B, Cristofolini L, Viceconti M. The material mapping strategy influences the accuracy of CT-based finite element models of bones: an evaluation against experimental measurements. Med Eng Phys. 2007;29(9):973–9.

    Article  PubMed  Google Scholar 

  102. Cheung AM, Adachi JD, Hanley DA, Kendler DL, Davison KS, Josse R, et al. High-resolution peripheral quantitative computed tomography for the assessment of bone strength and structure: a review by the Canadian Bone Strength Working Group. Curr Osteoporos Rep. 2013;11(2):136–46.

    Article  PubMed  PubMed Central  Google Scholar 

  103. Keyak JH, Rossi SA, Jones KA, Skinner HB. Prediction of femoral fracture load using automated finite element modeling. J Biomech. 1997;31(2):125–33.

    Article  Google Scholar 

  104. Dall’Ara E, Luisier B, Schmidt R, Kainberger F, Zysset P, Pahr D. A nonlinear QCT-based finite element model validation study for the human femur tested in two configurations in vitro. Bone. 2013;52(1):27–38.

    Article  PubMed  Google Scholar 

  105. Jones AC, Wilcox RK. Assessment of factors influencing finite element vertebral model predictions. J Biomech Eng. 2007;129(6):898–903.

    Article  PubMed  Google Scholar 

  106. Yosibash Z, Tal D, Trabelsi N. Predicting the yield of the proximal femur using high-order finite-element analysis with inhomogeneous orthotropic material properties. Philos Transact A Math Phys Eng Sci. 2010;368(1920):2707–23.

    Article  Google Scholar 

  107. Bevill G, Keaveny TM. Trabecular bone strength predictions using finite element analysis of micro-scale images at limited spatial resolution. Bone. 2009;44(4):579–84.

    Article  PubMed  Google Scholar 

  108. Currey JD. Power law models for the mechanical properties of cancellous bone. Eng Med. 1986;15(3):153–4.

    Article  CAS  PubMed  Google Scholar 

  109. Helgason B, Perilli E, Schileo E, Taddei F, Brynjolfsson S, Viceconti M. Mathematical relationships between bone density and mechanical properties: a literature review. Clin Biomech (Bristol, Avon). 2008;23(2):135–46.

    Article  Google Scholar 

  110. Enns-Bray WS, Owoc JS, Nishiyama KK, Boyd SK. Mapping anisotropy of the proximal femur for enhanced image based finite element analysis. J Biomech. 2014;47(13):3272–8.

    Article  PubMed  Google Scholar 

  111. Marangalou JH, Ito K, van Rietbergen B. A novel approach to estimate trabecular bone anisotropy from stress tensors. Biomech Model Mechanobiol. 2015;14(1):39–48.

    Article  Google Scholar 

  112. Zysset PK. A review of morphology-elasticity relationships in human trabecular bone: theories and experiments. J Biomech. 2003;36(10):1469–85.

    Article  PubMed  Google Scholar 

  113. Gross T, Pahr DH, Zysset PK. Morphology-elasticity relationships using decreasing fabric information of human trabecular bone from three major anatomical locations. Biomech Model Mechanobiol. 2013;12(4):793–800.

    Article  PubMed  Google Scholar 

  114. Bevill G, Easley SK, Keaveny TM. Side-artifact errors in yield strength and elastic modulus for human trabecular bone and their dependence on bone volume fraction and anatomic site. J Biomech. 2007;40:3381–8.

    Article  PubMed  PubMed Central  Google Scholar 

  115. Panyasantisuk J, Pahr DH, Gross T, Zysset PK. Comparison of mixed and kinematic uniform boundary conditions in homogenized elasticity of femoral trabecular bone using microfinite element analyses. J Biomech Eng (2015); 137(1). doi:10.1115/1.4028968.

  116. Austman RL, Milner JS, Holdsworth DW, Dunning CE. The effect of the density-modulus relationship selected to apply material properties in a finite element model of long bone. J Biomech. 2008;41:3171–6. doi:10.1016/j.jbiomech.2008.08.017.

    Article  PubMed  Google Scholar 

  117. Cong A, Buijs JO, Dragomir-Daescu D. In situ parameter identification of optimal density-elastic modulus relationships in subject-specific finite element models of the proximal femur. Med Eng Phys. 2011;33:164–73. doi:10.1016/j.medengphy.2010.09.018.

    Article  PubMed  Google Scholar 

  118. Eberle S, Gottlinger M, Augat P. An investigation to determine if a single validated density-elasticity relationship can be used for subject specific finite element analyses of human long bones. Med Eng Phys. 2013;35:875–83. doi:10.1016/j.medengphy.2012.08.

    Article  PubMed  Google Scholar 

  119. Marangalou JH, Ito K, Cataldi M, Taddei F, van Rietbergen B. A novel approach to estimate trabecular bone anisotropy using a database approach. J Biomech. 2013;46(14):2356–62.

    Article  Google Scholar 

  120. Pahr DH, Dall’Ara E, Varga P, Zysset PK. HR-pQCT-based homogenized finite element models provide quantitative predictions of experimental vertebral body stiffness and strength with the same accuracy as microFE models. Comput Methods Biomech Biomed Engin. 2012;15:711–20.

    Article  PubMed  Google Scholar 

  121. Kinzl M, Wolfram U, Pahr DH. Identification of a crushable foam material model and application to strength and damage prediction of human femur and vertebral body. J Mech Behav Biomed Mater. 2013;26:136–47.

    Article  CAS  PubMed  Google Scholar 

  122. Sanyal A, Scheffelin J, Keaveny TM. The quartic piecewise-linear criterion for the multiaxial yield behavior of human trabecular bone. J Biomech Eng, 2015; 137(1).

  123. Schwiedrzik JJ, Wolfram U, Zysset PK. A generalized anisotropic quadric yield criterion and its application to bone tissue at multiple length scales. Biomech Model Mechanobiol. 2013;12(6):1155–68.

    Article  CAS  PubMed  Google Scholar 

  124. Taylor WR, Heller MO, Bergmann G, Duda GN. Tibiofemoral loading during human gait and stair climbing. J Orthop Res. 2004;22(3):625–32.

    Article  PubMed  Google Scholar 

  125. Heller MO, Kratzenstein S, Ehrig RM, Wassilew G, Duda GN, Taylor WR. The weighted optimal common shape technique improves identification of the hip joint center of rotation in vivo. J Orthop Res. 2011;29(10):1470–5.

    Article  PubMed  Google Scholar 

  126. Falcinelli C, Schileo E, Balistreri L, Baruffaldi F, Bordini B, Viceconti M, et al. Multiple loading conditions analysis can improve the association between finite element bone strength estimates and proximal femur fractures: a preliminary study in elderly women. Bone. 2014;67:71–80.

    Article  PubMed  Google Scholar 

  127. Taddei F, Palmadori I, Taylor WR, Heller MO, Bordini B, Toni A, et al. Safety factor of the proximal femur during gait: a population-based finite element study. J Biomech. 2014;47(14):3433–40.

    Article  PubMed  Google Scholar 

  128. Christen P, Ito K, Knippels I, Müller R, van Lenthe GH, van Rietbergen B. Subject-specific bone loading estimation in the human distal radius. J Biomech. 2013;46(4):759–66.

    Article  PubMed  Google Scholar 

  129. Christen P, Ito K, Galis F, van Rietbergen B. Determination of hip-joint loading patterns of living and extinct mammals using an inverse wolf’s law approach. Biomech Model Mechanobiol. 2015;14(2):427–32.

    Article  PubMed  Google Scholar 

  130. Dall’Ara E, Schmidt R, Pahr D, Varga P, Chevalier Y, Patsch J, et al. A nonlinear finite element model validation study based on a novel experimental technique for inducing anterior wedge-shape fractures in human vertebral bodies in vitro. J Biomech. 2010;43:2374–80.

    Article  PubMed  Google Scholar 

  131. Varga P, Dall’Ara E, Pahr DH, Pretterkliebe M, Zysset PK. Validation of an HR-pQCT-based homogenized finite element approach using mechanical testing of ultra-distal radius sections. Biomech Model Mechanobiol. 2011;10(4):431–44.

    Article  PubMed  Google Scholar 

  132. Maquer G, Dall’Ara E, Zysset PK. Removal of the cortical endplates has little effect on ultimate load and damage distribution in QCT-based voxel models of human lumbar vertebrae under axial compression. J Biomech. 2012;45(9):1733–8.

    Article  PubMed  Google Scholar 

  133. Matsumoto T, Ohnishi I, Bessho M, Imai K, Ohashi S, Nakamura K. Prediction of vertebral strength under loading conditions occurring in activities of daily living using a computed tomography-based nonlinear finite element method. Spine. 2009;34(14):1464–9.

    Article  PubMed  Google Scholar 

  134. Nishiyama KK, Ito M, Harada A, Boyd SK. Classification of women with and without hip fracture based on quantitative computed tomography and finite element analysis. Osteoporos Int. 2014;25(2):619–26.

    Article  CAS  PubMed  Google Scholar 

  135. Keyak JH, Sigurdsson S, Karlsdottir GS, Oskarsdottir D, Sigmarsdottir A, Kornak J, et al. Effect of finite element model loading condition on fracture risk assessment in men and women: the AGES-Reykjavik study. Bone. 2013;57(1):18–29.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  136. Viceconti M, Taddei F, Cristofolini L, Martelli S, Falcinelli C, Schileo E. Are spontaneous fractures possible? An example of clinical application for personalised, multiscale neuro-musculo-skeletal modelling. J Biomech. 2012;45(3):421–6.

    Article  PubMed  Google Scholar 

  137. Keaveny TM, McClung MR, Genant HK, Zanchetta JR, Kendler D, Brown JP, et al. Femoral and vertebral strength improvements in postmenopausal women with osteoporosis treated with denosumab. J Bone Miner Res. 2014;29(1):158–65.

    Article  CAS  PubMed  Google Scholar 

  138. Vilayphiou N, Boutroy S, Sornay-Rendu E, van Rietbergen B, Munoz F, Delmas PD, et al. Finite element analysis performed on radius and tibia HR-pQCT images and fragility fractures at all sites in postmenopausal women. Bone. 2010;46(4):1030–7.

    Article  PubMed  Google Scholar 

  139. Anderson AE, Ellis BJ, Weiss JA. Verification, validation and sensitivity studies in computational biomechanics. Comput Methods Biomech Biomed Engin. 2007;10(3):171–84.

    Article  PubMed  PubMed Central  Google Scholar 

  140. Liebschner MA, Kopperdahl DL, Rosenberg WS, Keaveny TM. Finite element modeling of the human thoracolumbar spine. Spine. 2003;28(6):559–65.

    PubMed  Google Scholar 

  141. Cody DD, Gross GJ, Hou FJ, Spencer HJ, Goldstein SA, Fyhrie DP. Femoral strength is better predicted by finite element models than QCT and DXA. J Biomech. 1999;32(10):1013–20.

    Article  CAS  PubMed  Google Scholar 

  142. Dragomir-Daescu D, Op Den Buijs J, McEligot S, Dai Y, Entwistle RC, Salas C, et al. Robust QCT/FEA models of proximal femur stiffness and fracture load during a sideways fall on the hip. Ann Biomed Eng. 2011;39(2):742–55.

    Article  PubMed  Google Scholar 

  143. Pistoia W, van Rietbergen B, Lochmuller EM, Lill CA, Eckstein F, Ruegsegger P. Estimation of distal radius failure load with micro-finite element analysis models based on three-dimensional peripheral quantitative computed tomography images. Bone. 2002;30(6):842–8.

    Article  CAS  PubMed  Google Scholar 

  144. Varga P, Baumbach S, Pahr D, Zysset PK. Validation of an anatomy specific finite element model of Colles’ fracture. J Biomech. 2009;42(11):1726–31.

    Article  CAS  PubMed  Google Scholar 

  145. MacNeil JA, Boyd SK. Bone strength at the distal radius can be estimated from high-resolution peripheral quantitative computed tomography and the finite element method. Bone. 2008;42(6):1203–13.

    Article  PubMed  Google Scholar 

  146. Yosibash Z, Trabelsi N, Milgrom C. Reliable simulations of the human proximal femur by high-order finite element analysis validated by experimental observations. J Biomech. 2007;40(16):3688–99.

    Article  PubMed  Google Scholar 

  147. Zysset P, Qin L, Lang T, Khosla S, Leslie WD, Shepherd JA, et al. Clinical use of quantitative computed tomography-based finite element analysis of the hip and spine in the management of osteoporosis in adults: the 2015 ISCD Official Positions-Part II. J Clin Densitom. 2015;18(3):359–92. This position paper of the International Society for Clinical Densitometry (ISCD) provides recommendations for the clinical use of QCT-based finite element analysis in the management of osteoporosis with supporting medical evidence, rationale, controversy, and suggestions for further study.

    Article  PubMed  Google Scholar 

  148. Keaveny TM, Kopperdahl DL, Melton 3rd LJ, Hoffmann PF, Amin S, Riggs BL, et al. Age-dependence of femoral strength in white women and men. J Bone Miner Res. 2010;25(5):994–1001.

    PubMed  Google Scholar 

  149. Imai K, Ohnishi I, Matsumoto T, Yamamoto S, Nakamura K. Assessment of vertebral fracture risk and therapeutic effects of alendronate in postmenopausal women using a quantitative computed tomography-based nonlinear finite element method. Osteoporos Int. 2009;20(5):801–10.

    Article  CAS  PubMed  Google Scholar 

  150. Wang X, Sanyal A, Cawthon PM, Palermo L, Jekir M, Christensen J, et al. Prediction of new clinical vertebral fractures in elderly men using finite element analysis of CT scans. J Bone Miner Res. 2012;27(4):808–16.

  151. Graeff C, Marin F, Petto H, Kayser O, Reisinger A, Pena J, et al. High resolution quantitative computed tomography-based assessment of trabecular microstructure and strength estimates by finite-element analysis of the spine, but not DXA, reflects vertebral fracture status in men with glucocorticoid-induced osteoporosis. Bone. 2013;52(2):568–77.

    Article  CAS  PubMed  Google Scholar 

  152. Keyak JH, Sigurdsson S, Karlsdottir G, Oskarsdottir D, Sigmarsdottir A, Zhao S, et al. Male-female differences in the association between incident hip fracture and proximal femoral strength: a finite element analysis study. Bone. 2011;48(6):1239–45.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  153. Kopperdahl DL, Aspelund T, Hoffmann PF, Sigurdsson S, Siggeirsdottir K, Harris TB, et al. Assessment of incident spine and hip fractures in women and men using finite element analysis of CT scans. J Bone Miner Res. 2014;29(3):570–80.

    Article  PubMed  PubMed Central  Google Scholar 

  154. Langton CM, Pisharody S, Keyak JH. Comparison of 3D finite element analysis derived stiffness and BMD to determine the failure load of the excised proximal femur. Med Eng Phys. 2009;31(6):668–72.

    Article  CAS  PubMed  Google Scholar 

  155. Luo Y, Ferdous Z, Leslie WD. Precision study of DXA-based patient-specific finite element modeling for assessing hip fracture risk. Int J Numer Method Biomed Eng. 2013;29(5):615–29.

    Article  PubMed  Google Scholar 

  156. Yang L, Peel N, Clowes JA, McCloskey EV, Eastell R. Use of DXA-based structural engineering models of the proximal femur to discriminate hip fracture. J Bone Miner Res. 2009;24(1):33–42.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  157. MacNeil JA, Adachi JD, Goltzman D, Josse RG, Kovacs CS, Prior JC, et al. Predicting fracture using 2D finite element modelling. Med Eng Phys. 2012;34(4):478–84.

    Article  CAS  PubMed  Google Scholar 

  158. Danielson ME, Beck TJ, Karlamangla AS, Greendale GA, Atkinson EJ, Lian Y, et al. A comparison of DXA and CT based methods for estimating the strength of the femoral neck in post-menopausal women. Osteoporos Int. 2013;24(4):1379–88.

    Article  CAS  PubMed  Google Scholar 

  159. Thevenot J, Koivumaki J, Kuhn V, Eckstein F, Jamsa T. A novel methodology for generating 3D finite element models of the hip from 2D radiographs. J Biomech. 2014;47(2):438–44.

    Article  PubMed  Google Scholar 

Download references

Acknowledgments

The authors would like to thank their former PhD students at ILSB and ISTB for their contribution to the development of the FEA expertise that is continuously progressing in bone research and clinical studies. Grant no. 143769 from the Swiss National Science Foundation is gratefully acknowledged.

Author information

Authors and Affiliations

  1. Institute of Lightweight Design and Structural Biomechanics, Vienna University of Technology, Vienna, Austria

    Dieter H. Pahr

  2. Institute for Surgical Technology and Biomechanics, University of Bern, Bern, Switzerland

    Philippe K. Zysset

Authors
  1. Dieter H. Pahr
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Philippe K. Zysset
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Philippe K. Zysset.

Ethics declarations

Conflict of Interest

Dieter H. Pahr and Philippe K. Zysset declare that they have no conflict of interest.

Human and Animal Rights and Informed Consent

This article does not contain any studies with human or animal subjects performed by any of the authors.

Additional information

This article is part of the Topical Collection on Imaging

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Pahr, D.H., Zysset, P.K. Finite Element-Based Mechanical Assessment of Bone Quality on the Basis of In Vivo Images. Curr Osteoporos Rep 14, 374–385 (2016). https://doi.org/10.1007/s11914-016-0335-y

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11914-016-0335-y

Keywords

Search

Navigation