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What’s Next in the Field of Bone Health in Pediatrics? Research Considerations

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Bone Health Assessment in Pediatrics

Abstract

The aim of this chapter is to introduce promising new tools for the pediatric field able to assess bone microarchitectural structure and texture at the central and axial skeleton using Magnetic Resonance Imaging (MRI), Finite Element Analysis from computed tomography scans or Trabecular Bone Score (TBS) from Dual Energy X-Ray Absorptiometry (DXA) scans. Although all three of these technologies have been used more widely in adults, the promise of enhanced information and improved predictability of fracture offer complementary value to the areal Bone Mineral Density (aBMD) for the diagnosis of skeletal implications of multiple pathological conditions that may affect the skeletal development during growth. This chapter includes a description of each technology and mathematical framework as well as its clinical use in adults and potential applications and limitations for pediatric patients.

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References

  1. Rauch F, Tutlewski B, Fricke O, Rieger-Wettengl G, Schauseil-Zipf U, Herkenrath P, Neu CM, Schoenau E. Analysis of cancellous bone turnover by multiple slice analysis at distal radius: a study using peripheral quantitative computed tomography. J Clin Densitom. 2001;4(3):257–62.

    Article  CAS  PubMed  Google Scholar 

  2. Moxley 3rd RT, Pandya S. Weekend high-dosage prednisone: a new option for treatment of Duchenne muscular dystrophy. Neurology. 2011;77(5):416–7.

    Article  PubMed  Google Scholar 

  3. Viljakainen H, Korhonen T, Hytinantti T, et al. Maternal vitamin D status affects bone growth in early childhood—a prospective cohort study. Osteoporos Int. 2011;22:883–9.

    Article  CAS  PubMed  Google Scholar 

  4. Mansfield P, Morris PG. NMR imaging in biomedicine. New York: Academic Press; 1982.

    Google Scholar 

  5. Gatehouse PD, Bydder GM. Magnetic resonance imaging of short T2 components in tissue. Clin Radiol. 2003;58(1):1–19.

    Article  CAS  PubMed  Google Scholar 

  6. Gomberg BR, Saha PK, Wehrli FW. Method for cortical bone structural analysis from magnetic resonance images. Acad Radiol. 2005;12(10):1320–32.

    Article  PubMed  PubMed Central  Google Scholar 

  7. Elliott SR, Robinson RA. The water content of bone. I. The mass of water, inorganic crystals, organic matrix, and CO2 space components in a unit volume of the dog bone. J Bone Joint Surg Am. 1957;39-A(1):167–88.

    CAS  PubMed  Google Scholar 

  8. Mueller KH, Trias A, Ray RD. Bone density and compostiton. Age-related and pathological changes in water and mineral content. J Bone Joint Surg Am. 1966;48(1):140–8.

    CAS  PubMed  Google Scholar 

  9. Timmins PA, Wall JC. Bone water. Calcif Tissue Res. 1977;23(1):1–5.

    Article  CAS  PubMed  Google Scholar 

  10. Newitt DC, Van Rietbergen B, Majumdar S. Processing and analysis of in vivo high-resolution MR images of trabecular bone for longitudinal studies: reproducibility of structural measures and micro-finite element analysis derived mechanical properties. Osteoporos Int. 2002;13:278–87.

    Article  CAS  PubMed  Google Scholar 

  11. Hyun B, Newitt DC, Majumdar S. Assessment of cortical bone structure using high-resolution magnetic resonance imaging. In: Proceedings 13th scientific meeting, international society for magnetic resonance in medicine, Miami; 2005.

    Google Scholar 

  12. Hildebrand T, Ruegsegger P. A new method for the model-independent assessment of thickness in three-dimensional images. J Microsc. 1997;185:67–75.

    Article  Google Scholar 

  13. Kazakia GJ, et al. In vivo determination of bone structure in postmenopausal women: a comparison of HR-pQCT and high-field MR imaging. J Bone Miner Res. 2008;23(4):463–74.

    Article  PubMed  Google Scholar 

  14. Wehrli FW, et al. Quantitative MRI for the assessment of bone structure and function. NMR Biomed. 2006;19(7):731–64.

    Article  PubMed  Google Scholar 

  15. Wehrli FW. Structural and functional assessment of trabecular and cortical bone by micro magnetic resonance imaging. J Magn Reson Imaging. 2007;25(2):390–409.

    Article  PubMed  Google Scholar 

  16. Parfitt AM, et al. Relationships between surface, volume, and thickness of iliac trabecular bone in aging and in osteoporosis. Implications for the microanatomic and cellular mechanisms of bone loss. J Clin Invest. 1983;72(4):1396–409.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Chung HW, et al. Quantitative analysis of trabecular microstructure by 400 MHz nuclear magnetic resonance imaging. J Bone Miner Res. 1995;10(5):803–11.

    Article  CAS  PubMed  Google Scholar 

  18. Majumdar S, et al. Evaluation of technical factors affecting the quantification of trabecular bone structure using magnetic resonance imaging. Bone. 1995;17(4):417–30.

    Article  CAS  PubMed  Google Scholar 

  19. Majumdar S, et al. Correlation of trabecular bone structure with age, bone mineral density, and osteoporotic status: in vivo studies in the distal radius using high resolution magnetic resonance imaging. J Bone Miner Res. 1997;12(1):111–8.

    Article  CAS  PubMed  Google Scholar 

  20. Krug R, et al. Wavelet-based characterization of vertebral trabecular bone structure from magnetic resonance images at 3T compared with micro-computed tomographic measurements. Magn Reson Imaging. 2007;25(3):392–8.

    Article  PubMed  Google Scholar 

  21. Saha PK, Wehrli FW. Measurement of trabecular bone thickness in the limited resolution regime of in vivo MRI by fuzzy distance transform. IEEE Trans Med Imaging. 2004;23(1):53–62.

    Article  PubMed  Google Scholar 

  22. Amling M, et al. Architecture and distribution of cancellous bone yield vertebral fracture clues. A histomorphometric analysis of the complete spinal column from 40 autopsy specimens. Arch Orthop Trauma Surg. 1996;115(5):262–9.

    Article  CAS  PubMed  Google Scholar 

  23. Boyce RW, et al. Unbiased estimation of vertebral trabecular connectivity in calcium-restricted ovariectomized minipigs. Bone. 1995;16(6):637–42.

    Article  CAS  PubMed  Google Scholar 

  24. Kinney JH, Ladd AJ. The relationship between three-dimensional connectivity and the elastic properties of trabecular bone. J Bone Miner Res. 1998;13(5):839–45.

    Article  CAS  PubMed  Google Scholar 

  25. Saha PK, Chaudhuri BB. 3D digital topology under binary transformation with applications. Comput Vis Image Underst. 1996;63(3):418–29.

    Article  Google Scholar 

  26. Gomberg BR, et al. Topological analysis of trabecular bone MR images. IEEE Trans Med Imaging. 2000;19(3):166–74.

    Article  CAS  PubMed  Google Scholar 

  27. Pothuaud L, et al. In vivo application of 3D-line skeleton graph analysis (LSGA) technique with high-resolution magnetic resonance imaging of trabecular bone structure. Osteoporos Int. 2004;15(5):411–9.

    Article  PubMed  Google Scholar 

  28. Pothuaud L, et al. Three-dimensional-line skeleton graph analysis of high-resolution magnetic resonance images: a validation study from 34-microm-resolution microcomputed tomography. J Bone Miner Res. 2002;17(10):1883–95.

    Article  PubMed  Google Scholar 

  29. Pothuaud L, et al. Combination of topological parameters and bone volume fraction better predicts the mechanical properties of trabecular bone. J Biomech. 2002;35(8):1091–9.

    Article  PubMed  Google Scholar 

  30. Carballido-Gamio J, et al. Geodesic topological analysis of trabecular bone microarchitecture from high-spatial resolution magnetic resonance images. Magn Reson Med. 2009;61(2):448–56.

    Article  PubMed  PubMed Central  Google Scholar 

  31. Benito M, et al. Deterioration of trabecular architecture in hypogonadal men. J Clin Endocrinol Metab. 2003;88(4):1497–502.

    Article  CAS  PubMed  Google Scholar 

  32. Benito M, et al. Effect of testosterone replacement on trabecular architecture in hypogonadal men. J Bone Miner Res. 2005;20(10):1785–91.

    Article  CAS  PubMed  Google Scholar 

  33. Link TM, et al. Changes in calcaneal trabecular bone structure after heart transplantation: an MR imaging study. Radiology. 2000;217(3):855–62.

    Article  CAS  PubMed  Google Scholar 

  34. Link TM, et al. In vivo high resolution MRI of the calcaneus: differences in trabecular structure in osteoporosis patients. J Bone Miner Res. 1998;13(7):1175–82.

    Article  CAS  PubMed  Google Scholar 

  35. Link TM, et al. Changes in calcaneal trabecular bone structure assessed with high-resolution MR imaging in patients with kidney transplantation. Osteoporos Int. 2002;13(2):119–29.

    Article  CAS  PubMed  Google Scholar 

  36. Majumdar S, et al. Trabecular bone architecture in the distal radius using magnetic resonance imaging in subjects with fractures of the proximal femur. Magnetic Resonance Science Center and Osteoporosis and Arthritis Research Group. Osteoporos Int. 1999;10(3):231–9.

    Article  CAS  PubMed  Google Scholar 

  37. Wehrli FW, et al. Digital topological analysis of in vivo magnetic resonance microimages of trabecular bone reveals structural implications of osteoporosis. J Bone Miner Res. 2001;16(8):1520–31.

    Article  CAS  PubMed  Google Scholar 

  38. Wehrli FW, et al. Cancellous bone volume and structure in the forearm: noninvasive assessment with MR microimaging and image processing. Radiology. 1998;206(2):347–57.

    Article  CAS  PubMed  Google Scholar 

  39. Wehrli FW, et al. Quantitative high-resolution magnetic resonance imaging reveals structural implications of renal osteodystrophy on trabecular and cortical bone. J Magn Reson Imaging. 2004;20(1):83–9.

    Article  PubMed  Google Scholar 

  40. Wehrli FW, et al. Role of magnetic resonance for assessing structure and function of trabecular bone. Top Magn Reson Imaging. 2002;13(5):335–55.

    Article  PubMed  Google Scholar 

  41. Techawiboonwong A, et al. Cortical bone water: in vivo quantification with ultrashort echo-time MR imaging. Radiology. 2008;248(3):824–33.

    Article  PubMed  PubMed Central  Google Scholar 

  42. Chesnut 3rd CH, et al. Effects of salmon calcitonin on trabecular microarchitecture as determined by magnetic resonance imaging: results from the QUEST study. J Bone Miner Res. 2005;20(9):1548–61.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Wehrli FW, et al. In vivo magnetic resonance detects rapid remodeling changes in the topology of the trabecular bone network after menopause and the protective effect of estradiol. J Bone Miner Res. 2008;23(5):730–40.

    Article  PubMed  PubMed Central  Google Scholar 

  44. Modlesky CM, et al. Evaluation of the femoral midshaft in children with cerebral palsy using magnetic resonance imaging. Osteoporos Int. 2009;20(4):609–15.

    Article  CAS  PubMed  Google Scholar 

  45. Modlesky CM, et al. Sex differences in trabecular bone microarchitecture are not detected in pre and early pubertal children using magnetic resonance imaging. Bone. 2011;49(5):1067–72.

    Article  PubMed  PubMed Central  Google Scholar 

  46. Modlesky CM, Subramanian P, Miller F. Underdeveloped trabecular bone microarchitecture is detected in children with cerebral palsy using high-resolution magnetic resonance imaging. Osteoporos Int. 2008;19(2):169–76.

    Article  CAS  PubMed  Google Scholar 

  47. Goldenstein J, Kazakia G, Majumdar S. In vivo evaluation of the presence of bone marrow in cortical porosity in postmenopausal osteopenic women. Ann Biomed Eng. 2010;38(2):235–46.

    Article  PubMed  Google Scholar 

  48. Kindler JM, Ross HL, Laing EM, Modlesky CM, Pollock NK, Baile CA, Lewis RD. Load-specific physical activity scores are related to tibia bone architecture. Int J Sport Nutr Exerc Metab. 2014;25(2):136–44.

    Article  PubMed  Google Scholar 

  49. 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 

  50. Cody DD, et al. 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 

  51. 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 

  52. Keyak JH, et al. Automated 3-dimensional finite-element modeling of bone—a new method. J Biomed Eng. 1990;12(5):389–97.

    Article  CAS  PubMed  Google Scholar 

  53. Keaveny TM. Biomechanical computed tomography-noninvasive bone strength analysis using clinical computed tomography scans. Ann N Y Acad Sci. 2010;1192:57–65.

    Article  PubMed  Google Scholar 

  54. Zysset PK, et al. Finite element analysis for prediction of bone strength. Bonekey Rep. 2013;2:386.

    Article  PubMed  PubMed Central  Google Scholar 

  55. Pistoia W, et al. 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 

  56. Reddy JN. An introduction to the finite element method. 3rd ed. New York, NY: McGraw-Hill Higher Education; 2006. p. 766.

    Google Scholar 

  57. Engelke K, et al. Advanced CT based in vivo methods for the assessment of bone density, structure, and strength. Curr Osteoporos Rep. 2013;11(3):246–55.

    Article  CAS  PubMed  Google Scholar 

  58. Weber NK, et al. Validation of a CT-derived method for osteoporosis screening in IBD patients undergoing contrast-enhanced CT enterography. Am J Gastroenterol. 2014;109(3):401–8.

    Article  PubMed  PubMed Central  Google Scholar 

  59. Khoo BC, et al. Comparison of QCT-derived and DXA-derived areal bone mineral density and T scores. Osteoporos Int. 2009;20(9):1539–45.

    Article  CAS  PubMed  Google Scholar 

  60. Keyak JH, 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 

  61. Kopperdahl DL, 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 

  62. Keaveny TM, et al. Comparison of the effects of teriparatide and alendronate on parameters of total hip strength as assessed by finite element analysis: results from the Forteo and Alendronate comparison trial. J Bone Miner Res. 2007;22:S26.

    Google Scholar 

  63. Keaveny TM, et al. Femoral bone strength and its relation to cortical and trabecular changes after treatment with PTH, alendronate, and their combination as assessed by finite element analysis of quantitative CT scans. J Bone Miner Res. 2008;23(12):1974–82.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Melton 3rd LJ, et al. Relation of vertebral deformities to bone density, structure, and strength. J Bone Miner Res. 2010;25(9):1922–30.

    Article  PubMed  PubMed Central  Google Scholar 

  65. Cheung AM, 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 

  66. van Rietbergen B, Ito K. A survey of micro-finite element analysis for clinical assessment of bone strength: the first decade. J Biomech. 2015;48(5):832–41.

    Article  PubMed  Google Scholar 

  67. Bevill G, et al. The influence of boundary conditions and loading mode on high-resolution finite element-computed trabecular tissue properties. Bone. 2009;44(4):573–8.

    Article  PubMed  Google Scholar 

  68. Fields AJ, et al. Vertebral fragility and structural redundancy. J Bone Miner Res. 2012;27(10):2152–8.

    Article  PubMed  PubMed Central  Google Scholar 

  69. Nawathe S, et al. Microstructural failure mechanisms in the human proximal femur for sideways fall loading. J Bone Miner Res. 2014;29(2):507–15.

    Article  PubMed  Google Scholar 

  70. Prevrhal S, et al. Accuracy of CT-based thickness measurement of thin structures: modeling of limited spatial resolution in all three dimensions. Med Phys. 2003;30(1):1–8.

    Article  PubMed  Google Scholar 

  71. Zadpoor AA, Weinans H. Patient-specific bone modeling and analysis: the role of integration and automation in clinical adoption. J Biomech. 2015;48(5):750–60.

    Article  PubMed  Google Scholar 

  72. Mueller TL, et al. Computational finite element bone mechanics accurately predicts mechanical competence in the human radius of an elderly population. Bone. 2011;48(6):1232–8.

    Article  PubMed  Google Scholar 

  73. 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 

  74. Christen D, et al. Improved fracture risk assessment based on nonlinear micro-finite element simulations from HRpQCT images at the distal radius. J Bone Miner Res. 2013;28(12):2601–8.

    Article  PubMed  Google Scholar 

  75. Varga P, et al. HR-pQCT based FE analysis of the most distal radius section provides an improved prediction of Colles’ fracture load in vitro. Bone. 2010;47(5):982–8.

    Article  PubMed  Google Scholar 

  76. Boutroy S, et al. Finite element analysis based on in vivo HR-pQCT images of the distal radius is associated with wrist fracture in postmenopausal women. J Bone Miner Res. 2008;23(3):392–9.

    Article  PubMed  Google Scholar 

  77. Melton 3rd LJ, et al. Assessing forearm fracture risk in postmenopausal women. Osteoporos Int. 2010;21(7):1161–9.

    Article  PubMed  Google Scholar 

  78. Vilayphiou N, 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 

  79. Vilayphiou N, et al. Finite element analysis performed on radius and tibia HR-pQCT images and fragility fractures at all sites in men. J Bone Miner Res. 2011;26(5):965–73.

    Article  PubMed  Google Scholar 

  80. Nishiyama KK, et al. Women with previous fragility fractures can be classified based on bone microarchitecture and finite element analysis measured with HR-pQCT. Osteoporos Int. 2013;24(5):1733–40.

    Article  CAS  PubMed  Google Scholar 

  81. Tsai JN, et al. Comparative effects of teriparatide, denosumab, and combination therapy on peripheral compartmental bone density, microarchitecture, and estimated strength: the DATA-HRpQCT Study. J Bone Miner Res. 2015;30(1):39–45.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  82. Burghardt AJ, et al. A longitudinal HR-pQCT study of alendronate treatment in postmenopausal women with low bone density: relations among density, cortical and trabecular microarchitecture, biomechanics, and bone turnover. J Bone Miner Res. 2010;25(12):2558–71.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  83. Hansen S, et al. Differing effects of PTH 1-34, PTH 1-84, and zoledronic acid on bone microarchitecture and estimated strength in postmenopausal women with osteoporosis: an 18-month open-labeled observational study using HR-pQCT. J Bone Miner Res. 2013;28(4):736–45.

    Article  CAS  PubMed  Google Scholar 

  84. Nishiyama KK, et al. Teriparatide increases strength of the peripheral skeleton in premenopausal women with idiopathic osteoporosis: a pilot HR-pQCT study. J Clin Endocrinol Metab. 2014;99(7):2418–25.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  85. Cheung AM, et al. Effects of odanacatib on the radius and tibia of postmenopausal women: improvements in bone geometry, microarchitecture, and estimated bone strength. J Bone Miner Res. 2014;29(8):1786–94.

    Article  CAS  PubMed  Google Scholar 

  86. Leung KS, et al. Structural, densitometric and biomechanical evaluations of Chinese patients with long-term bisphosphonate treatment. Chin Med J (Engl). 2013;126(1):27–33.

    CAS  Google Scholar 

  87. Tang XL, et al. Alterations of bone geometry, density, microarchitecture, and biomechanical properties in systemic lupus erythematosus on long-term glucocorticoid: a case-control study using HR-pQCT. Osteoporos Int. 2013;24(6):1817–26.

    Article  CAS  PubMed  Google Scholar 

  88. Cabal A, et al. High-resolution peripheral quantitative computed tomography and finite element analysis of bone strength at the distal radius in ovariectomized adult rhesus monkey demonstrate efficacy of odanacatib and differentiation from alendronate. Bone. 2013;56(2):497–505.

    Article  CAS  PubMed  Google Scholar 

  89. Dall'Ara E, et al. 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 

  90. Keyak JH. Improved prediction of proximal femoral fracture load using nonlinear finite element models. Med Eng Phys. 2001;23(3):165–73.

    Article  CAS  PubMed  Google Scholar 

  91. Keyak JH, et al. Prediction of femoral fracture load using automated finite element modeling. J Biomech. 1998;31(2):125–33.

    Article  CAS  PubMed  Google Scholar 

  92. Bessho M, et al. Prediction of strength and strain of the proximal femur by a CT-based finite element method. J Biomech. 2007;40(8):1745–53.

    Article  PubMed  Google Scholar 

  93. Koivumaki JE, et al. Ct-based finite element models can be used to estimate experimentally measured failure loads in the proximal femur. Bone. 2012;50(4):824–9.

    Article  PubMed  Google Scholar 

  94. Dragomir-Daescu D, 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 

  95. Duchemin L, et al. An anatomical subject-specific FE-model for hip fracture load prediction. Comput Methods Biomech Biomed Engin. 2008;11(2):105–11.

    Article  CAS  PubMed  Google Scholar 

  96. van den Munckhof S, Zadpoor AA. How accurately can we predict the fracture load of the proximal femur using finite element models? Clin Biomech (Bristol, Avon). 2014;29(4):373–80.

    Article  Google Scholar 

  97. Wang X, 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.

    Article  PubMed  PubMed Central  Google Scholar 

  98. Dall'Ara E, 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(12):2374–80.

    Article  PubMed  Google Scholar 

  99. Martin H, et al. 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 

  100. Buckley JM, Loo K, Motherway J. Comparison of quantitative computed tomography-based measures in predicting vertebral compressive strength. Bone. 2007;40(3):767–74.

    Article  PubMed  Google Scholar 

  101. Imai K, et al. Nonlinear finite element model predicts vertebral bone strength and fracture site. Spine. 2006;31(16):1789–94.

    Article  PubMed  Google Scholar 

  102. Melton LJ, et al. Structural determinants of vertebral fracture risk. J Bone Miner Res. 2007;22(12):1885–92.

    Article  PubMed  Google Scholar 

  103. Imai K, et al. 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 

  104. Anderson DE, et al. The associations between QCT-based vertebral bone measurements and prevalent vertebral fractures depend on the spinal locations of both bone measurement and fracture. Osteoporos Int. 2014;25(2):559–66.

    Article  CAS  PubMed  Google Scholar 

  105. Orwoll ES, et al. Finite element analysis of the proximal femur and hip fracture risk in older men. J Bone Miner Res. 2009;24(3):475–83.

    Article  PubMed  Google Scholar 

  106. Chevalier Y, et al. Biomechanical effects of teriparatide in women with osteoporosis treated previously with alendronate and risedronate: results from quantitative computed tomography-based finite element analysis of the vertebral body. Bone. 2010;46(1):41–8.

    Article  CAS  PubMed  Google Scholar 

  107. Graeff C, et al. Improvements in vertebral body strength under teriparatide treatment assessed in vivo by finite element analysis: results from the EUROFORS study. J Bone Miner Res. 2009;24(10):1672–80.

    Article  CAS  PubMed  Google Scholar 

  108. Imai K. Vertebral fracture risk and alendronate effects on osteoporosis assessed by a computed tomography-based nonlinear finite element method. J Bone Miner Metab. 2011;29(6):645–51.

    Article  CAS  PubMed  Google Scholar 

  109. Gluer CC, et al. Comparative effects of teriparatide and risedronate in glucocorticoid-induced osteoporosis in men: 18-month results of the EuroGIOPs trial. J Bone Miner Res. 2013;28(6):1355–68.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  110. Keaveny TM, et al. Effects of teriparatide and alendronate on vertebral strength as assessed by finite element modeling of QCT scans in women with osteoporosis. J Bone Miner Res. 2007;22(1):149–57.

    Article  CAS  PubMed  Google Scholar 

  111. Mawatari T, et al. Vertebral strength changes in rheumatoid arthritis patients treated with alendronate, as assessed by finite element analysis of clinical computed tomography scans: a prospective randomized clinical trial. Arthritis Rheum. 2008;58(11):3340–9.

    Article  CAS  PubMed  Google Scholar 

  112. Lewiecki EM, et al. Once-monthly oral ibandronate improves biomechanical determinants of bone strength in women with postmenopausal osteoporosis. J Clin Endocrinol Metab. 2009;94(1):171–80.

    Article  CAS  PubMed  Google Scholar 

  113. Keaveny TM, et al. Femoral strength in osteoporotic women treated with teriparatide or alendronate. Bone. 2012;50(1):165–70.

    Article  CAS  PubMed  Google Scholar 

  114. Brixen K, et al. Bone density, turnover, and estimated strength in postmenopausal women treated with odanacatib: a randomized trial. J Clin Endocrinol Metab. 2013;98(2):571–80.

    Article  CAS  PubMed  Google Scholar 

  115. Cosman F, et al. Hip and spine strength effects of adding versus switching to teriparatide in postmenopausal women with osteoporosis treated with prior alendronate or raloxifene. J Bone Miner Res. 2013;28(6):1328–36.

    Article  CAS  PubMed  Google Scholar 

  116. Keaveny TM, 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 

  117. Orwoll ES, et al. Evaluation of teriparatide treatment in adults with osteogenesis imperfecta. J Clin Invest. 2014;124(2):491–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  118. Kleerekoper M, et al. Assessing the effects of teriparatide treatment on bone mineral density, bone microarchitecture, and bone strength. J Bone Joint Surg Am. 2014;96(11):e90.

    Article  PubMed  Google Scholar 

  119. Engelke K, et al. Clinical use of quantitative computed tomography and peripheral quantitative computed tomography in the management of osteoporosis in adults: the 2007 ISCD Official Positions. J Clin Densitom. 2008;11(1):123–62.

    Article  PubMed  Google Scholar 

  120. American College of Radiology. Practice parameter for the performance of quantitative computed tomography (QCT) bone densitometry. Amended. 2014;39:1–14.

    Google Scholar 

  121. Farr JN, et al. Body composition during childhood and adolescence: relations to bone strength and microstructure. J Clin Endocrinol Metab. 2014;99(12):4641–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  122. Faje AT, et al. Adolescent girls with anorexia nervosa have impaired cortical and trabecular microarchitecture and lower estimated bone strength at the distal radius. J Clin Endocrinol Metab. 2013;98(5):1923–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  123. Ackerman KE, et al. Fractures in relation to menstrual status and bone parameters in young athletes. Med Sci Sports Exerc. 2015;47(8):1577–86.

    Article  PubMed  PubMed Central  Google Scholar 

  124. Caouette C, et al. Biomechanical analysis of fracture risk associated with tibia deformity in children with osteogenesis imperfecta: a finite element analysis. J Musculoskelet Neuronal Interact. 2014;14(2):205–12.

    CAS  PubMed  Google Scholar 

  125. Chevalley T, et al. Tracking of environmental determinants of bone structure and strength development in healthy boys: an eight-year follow up study on the positive interaction between physical activity and protein intake from prepuberty to mid-late adolescence. J Bone Miner Res. 2014;29(10):2182–92.

    Article  CAS  PubMed  Google Scholar 

  126. Farr JN, et al. Bone strength and structural deficits in children and adolescents with a distal forearm fracture resulting from mild trauma. J Bone Miner Res. 2014;29(3):590–9.

    Article  PubMed  PubMed Central  Google Scholar 

  127. Gabel L, et al. Bone architecture and strength in the growing skeleton: the role of sedentary time. Med Sci Sports Exerc. 2015;47(2):363–72.

    Article  PubMed  Google Scholar 

  128. Li X, et al. Developing CT based computational models of pediatric femurs. J Biomech. 2015;48(10):2034–40.

    Article  PubMed  Google Scholar 

  129. Singhal V, et al. Irisin levels are lower in young amenorrheic athletes compared with eumenorrheic athletes and non-athletes and are associated with bone density and strength estimates. PLoS One. 2014;9(6):e100218.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  130. Pothuaud L, Carceller P, et al. Correlations between grey-level variations in 2D projection images (TBS) and 3D microarchitecture: applications in the study of human trabecular bone microarchitecture. Bone. 2008;42:775–87.

    Article  PubMed  Google Scholar 

  131. Hans D, Barthe N, et al. Correlations between TBS, measured using antero-posterior DXA acquisition, and 3D parameters of bone micro-architecture: an experimental study on human cadavre vertebrae. J Clin Densitom. 2011;14(3):302–11.

    Article  PubMed  Google Scholar 

  132. Winzenrieth R, Michelet F, et al. Three-dimensional (3D) microarchitecture correlations with 2D projection image gray-level variations assessed by trabecular bone score using high-resolution computed tomographic acquisitions: effects of resolution and noise. J Clin Densitom. 2013;16(3):287–96.

    Article  PubMed  Google Scholar 

  133. Mandelbrot B, Van Ness J, et al. Fractional Brownian motions, fractional noises and applications. SIAM Rev. 1968;10:422–37.

    Article  Google Scholar 

  134. Coeurjolly J-F. Simulation and identification of the fractional Brownian motion: a bibliographical and comparative study. J Statist Soft. 2000;5:1–53.

    Article  Google Scholar 

  135. Bardet J-M, Lang G, et al. Semi-parametric estimation of the long-range dependence parameter: a survey. In: Doukhan P, Oppenheim G, Taqqu MS, editors. Theory and applications of long-range dependence. Boston: Birkhäuser; 2003. p. 557–77.

    Google Scholar 

  136. Olea RA. Fundamentals of semivariogram estimation, modeling, and usage. In: Yarus JM, Chambers RL, editors. Stochastic modeling and geostatistics, vol. 3. Tulsa, OK: AAPG Computer Applications in Geology; 1994. p. 27–35.

    Google Scholar 

  137. Kelkar M, Shibli S. Description of reservoir properties using fractals. In: Yarus JM, Chambers RL, editors. Stochastic modeling and geostatistics, vol. 3. Tulsa OK: AAPG Publication; 1994. p. 261.

    Google Scholar 

  138. Silva BC, Boutroy S, et al. Trabecular bone score (TBS)—a novel method to evaluate bone microarchitectural texture in patients with primary hyperparathyroidism. J Clin Endocrinol Metab. 2013;98(5):1963–70.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  139. Popp AW, Buffat H, et al. Microstructural parameters of bone evaluated using HR-pQCT correlate with the DXA-derived cortical index and the trabecular bone score in a cohort of randomly selected premenopausal women. PLoS One. 2014;9(2):e88946.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  140. Kocijan R, Muschitz C, et al. Bone structure assessed by HR-pQCT, TBS and DXL in adult patients with different types of osteogenesis imperfecta. Osteoporos Int. 2015. [Epub ahead of print].

    Google Scholar 

  141. Muschitz C, Kocijan R, et al. TBS reflects trabecular microarchitecture in remenopausal women and men with idiopathic osteoporosis and low-traumatic fractures. Bone. 2015;79:259–66.

    Article  PubMed  Google Scholar 

  142. Silva BC, Leslie WD, et al. Trabecular bone score: a noninvasive analytical method based upon the DXA image. J Bone Miner Res. 2014;29:518–30.

    Article  PubMed  Google Scholar 

  143. Hans D, Goertzen AL, et al. Bone microarchitecture assessed by TBS predicts osteoporotic fractures independent of bone density: the Manitoba study. J Bone Miner Res. 2011;26(11):2762–9.

    Article  PubMed  Google Scholar 

  144. Briot K, Paternotte S, et al. Added value of trabecular bone score to bone mineral density for prediction of osteoporotic fractures in postmenopausal women: the OPUS study. Bone. 2013;57(1):232–6.

    Article  PubMed  Google Scholar 

  145. Iki M, Tamaki J, et al. Trabecular bone score (TBS) predicts vertebral fractures in Japanese women over 10 years independently of bone density and prevalent vertebral deformity: the Japanese population-based osteoporosis (JPOS) cohort study. J Bone Miner Res. 2014;29(2):399–407.

    Article  PubMed  Google Scholar 

  146. Leslie WD, Krieg MA, et al. Clinical factors associated with trabecular bone score. J Clin Densitom. 2013;16(3):374–9.

    Article  PubMed  Google Scholar 

  147. Boutroy S, Hans D, et al. Trabecular bone score improves fracture risk prediction in non-osteoporotic women: the OFELY study. Osteoporos Int. 2013;24(1):77–85.

    Article  CAS  PubMed  Google Scholar 

  148. Harvey NC, Glüer CC, et al. Trabecular bone score (TBS) as a new complementary approach for osteoporosis evaluation in clinical practice. Bone. 2015;78:216–24.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  149. Dhaliwal R, Cibula D, Ghosh C, Weinstock RS, Moses AM. Bone quality assessment in type 2 diabetes mellitus. Osteoporos Int. 2014;25(7):1969–73.

    Article  CAS  PubMed  Google Scholar 

  150. Leslie WD, Aubry-Rozier B, Lamy O, Hans D, Manitoba Bone Density Program. TBS (trabecular bone score) and diabetes-related fracture risk. J Clin Endocrinol Metab. 2013;98(2):602–9.

    Article  CAS  PubMed  Google Scholar 

  151. Kim JH, Choi HJ, Ku EJ, Kim KM, Kim SW, Cho NH, Shin CS. Trabecular bone score as an indicator for skeletal deterioration in diabetes. J Clin Endocrinol Metab. 2015;100(2):475–82.

    Article  CAS  PubMed  Google Scholar 

  152. Paggiosi MA, Peel NF, et al. The impact of glucocorticoid therapy on trabecular bone score in older women. Osteoporos Int. 2015;26(6):1773–80.

    Article  CAS  PubMed  Google Scholar 

  153. Leib E, Winzenrieth R. Bone status in glucocorticoid treated men and women. Osteoporos Int. 2015;8. Epub ahead of print.

    Google Scholar 

  154. Eller-Vainicher C, Filopanti M, et al. Bone quality, as measured by trabecular bone score, in patients with primary hyperparathyroidism. Eur J Endocrinol. 2013;169:155–62.

    Article  CAS  PubMed  Google Scholar 

  155. Romagnoli E, Cipriani C, et al. “Trabecular Bone Score” (TBS): an indirect measure of bone micro-architecture in postmenopausal patients with primary hyperparathyroidism. Bone. 2013;53:154–9.

    Article  PubMed  Google Scholar 

  156. Dufour R, Winzenrieth R, et al. Generation and validation of a normative, age-specific reference curve for lumbar spine trabecular bone score (TBS) in French women. Osteoporos Int. 2013;24:2837–46.

    Article  CAS  PubMed  Google Scholar 

  157. Kolta S, Briot K, et al. TBS result is not affected by lumbar spine osteoarthritis. Osteoporos Int. 2014;25:1759–64.

    Article  CAS  PubMed  Google Scholar 

  158. Popp AW, Guler S, et al. Effects of zoledronate versus placebo on spine bone mineral density and microarchitecture assessed by the trabecular bone score in postmenopausal women with osteoporosis: a three-year study. J Bone Miner Res. 2013;28(3):449–54.

    Article  CAS  PubMed  Google Scholar 

  159. Senn C, Günther B, et al. Comparative effects of teriparatide and ibandronate on spine bone mineral density (BMD) and microarchitecture (TBS) in postmenopausal women with osteoporosis: a 2-year open-label study. Osteoporos Int. 2014;25(7):1945–51.

    Article  CAS  PubMed  Google Scholar 

  160. Di Gregorio S, Del Rio L, et al. Comparison between different bone treatments on areal bone mineral density (aBMD) and bone microarchitectural texture as assessed by the trabecular bone score (TBS). Bone. 2015;75:138–43.

    Article  PubMed  Google Scholar 

  161. Silva BC, Broy SB, et al. Fracture risk prediction by non-BMD DXA measures: the 2015 ISCD Official Positions part 2: trabecular bone score. J Clin Densitom. 2015;18(3):309–30.

    Article  PubMed  Google Scholar 

  162. http://www.dv-osteologie.org/dvo_leitlinien/osteoporose-leitlinie-2014.

  163. Donaldson AA, Feldman HA, et al. Spinal bone texture assessed by trabecular bone score in adolescent girls with anorexia nervosa. J Clin Endocrinol Metab. 2015;100(9):3436–42.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  164. Heiniö L, Nikander R, et al. Association between long-term exercise loading and lumbar spine trabecular bone score (TBS) in different exercise loading groups. J Musculoskelet Neuronal Interact. 2015;15(3):279–85.

    PubMed  Google Scholar 

  165. Shawwa K, Arabi A, et al. Predictors of trabecular bone score in school children. Osteoporos Int. 2015. [Epub ahead of print].

    Google Scholar 

  166. Winzenrieth R, Cormier C, et al. Influence of age and gender on spine bone density and TBS microarchitectural texture parameters in infants. Rotterdam, The Netherlands: ICCBH; 2013.

    Google Scholar 

  167. Del Rio L, Di Gregorio S, et al. Bone microarchitecture (TBS) and bone mass development during childhood and adolescence in a Spanish population group. Sevilla, Spain: ECCEO-IOF Congress; 2014.

    Google Scholar 

  168. Del Rio L, Winzenrieth R, et al. Bone quality and quantity in Duchenne muscular dystrophy patients. Salzburg, Austria: ICCBH; 2015.

    Google Scholar 

  169. Del Rio L, Winzenrieth R, et al. Evolution of bone quality and quantity in patients suffering from Duchenne muscular dystrophy. Salzburg, Austria: ICCBH; 2015.

    Google Scholar 

  170. Libber J, Winzenrieth R, et al. TBS increases over time in pre-teen girls. Salzburg, Austria: ICCBH; 2015.

    Google Scholar 

  171. Roschger P, Grabner BM, et al. Structural development of the mineralized tissue in the human L4 vertebral body. J Struct Biol. 2001;136(2):126–36.

    Article  CAS  PubMed  Google Scholar 

  172. Taylor JR, Twomey LT. Sexual dimorphism in human vertebral body shape. J Anat. 1984;138(Pt 2):281–6.

    PubMed  PubMed Central  Google Scholar 

  173. Peters JR, Chandrasekaran C, et al. Age- and gender-related changes in pediatric thoracic vertebral morphology. Spine J. 2015;15(5):1000–20.

    Article  PubMed  Google Scholar 

  174. Seeman E. Structural basis of growth-related gain and age-related loss of bone strength. Rheumatology (Oxford). 2008;47 Suppl 4:iv2–8.

    Google Scholar 

  175. Roschger P, Paschalis E, et al. Bone mineralization density distribution in health and disease. Bone. 2008;42:456–66.

    Article  CAS  PubMed  Google Scholar 

  176. Komarova SV, Safranek L, et al. Mathematical model for bone mineralization. Front Cell Dev Biol. 2015;3:51.

    Article  PubMed  PubMed Central  Google Scholar 

  177. Kroger H, Kotaniemi A, et al. Bone densitometry of the spine and femur in children by dual-energy x-ray absorptiometry. Bone Miner. 1992;17:75–85.

    Article  CAS  PubMed  Google Scholar 

  178. Katzman DK, Bachrach LK. Clinical and anthropometric correlates of bone mineral acquisition in healthy adolescent girls. J Clin Endocrinol Metab. 1991;73(6):1332–9.

    Article  CAS  PubMed  Google Scholar 

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Authors and Affiliations

  1. Radiology and Biomedical Imaging, University of California San Francisco, 1700 4th Street, 203 D Byers Hall, San Francisco, CA, 94502, USA

    Sharmila Majumdar Ph.D.

  2. Mechanical Engineering and Bioengineering, University of California, 5140 Etcheverry Hall, MC 1740, Berkeley, CA, 94720-1740, USA

    Tony M. Keavney Ph.D.

  3. Department of Bone Densitometry, Hospital Sant Joan De Deu, Londres 6, Passeig De Sant Joan De Deu, 2 , Esplugues de Llobregat, Barcelona, 08029, Spain

    Luis Del Rio M.D., C.C.D.

  4. Department of Rare Skeletal Disease, Children’s Hospital, University of Cologne, Kerpenerstr. 62, Cologne, 50931, Germany

    Oliver Semler M.D.

  5. Research and Development Department, Medimaps SASU, 5 Avenue Henri Becquerel, Merignac, France

    Renaud Winzenrieth Ph.D.

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  1. Sharmila Majumdar Ph.D.
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  2. Tony M. Keavney Ph.D.
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  3. Luis Del Rio M.D., C.C.D.
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  4. Oliver Semler M.D.
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  5. Renaud Winzenrieth Ph.D.
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Correspondence to Sharmila Majumdar Ph.D. .

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Editors and Affiliations

  1. UCSF Benioff Children’s Hospital Oakland, Children’s Hospital Oakland Research Institute, Oakland, California, USA

    Ellen B. Fung

  2. Department of Pediatrics, Stanford University School of Medicine, Stanford, California, USA

    Laura K. Bachrach

  3. Department of Orthopaedic Surgery, University of California, San Francisco, USA

    Aenor J. Sawyer

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Majumdar, S., Keavney, T.M., Del Rio, L., Semler, O., Winzenrieth, R. (2016). What’s Next in the Field of Bone Health in Pediatrics? Research Considerations. In: Fung, E., Bachrach, L., Sawyer, A. (eds) Bone Health Assessment in Pediatrics. Springer, Cham. https://doi.org/10.1007/978-3-319-30412-0_13

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