• Thomas Baum
  • Dimitrios C. Karampinos
  • Stefan Ruschke
  • Hans Liebl
  • Peter B. Noël
  • Jan S. Bauer
Part of the Lecture Notes in Computational Vision and Biomechanics book series (LNCVB, volume 18)


Osteoporosis is defined as a skeletal disorder characterized by compromised bone strength predisposing an individual to an increased risk for fracture. Osteoporotic fractures, in particular spine fractures, are associated with a high mortality and generate immense financial costs. Osteoporotic vertebral fractures frequently occur in absence of a specific trauma and may be asymptomatic. Since a prevalent vertebral fracture increases the risk of a subsequent fracture, the diagnosis of osteoporotic vertebral fractures is highly important to initiate appropriate therapy. Computer-assisted diagnostic tools for spine radiographs, dual-energy X-ray absorptiometry (DXA) and multi-detector computed tomography (MDCT) images have been developed to support radiologists to correctly diagnose and report osteoporotic vertebral fractures. The assessment of fracture risk at the spine has traditionally relied on the measurements of bone mineral density (BMD) by using DXA. However, BMD values of subjects with versus without osteoporotic fractures overlap. Bone strength reflects the integration of BMD and bone quality. The latter can be partly determined by measurements of bone microstructure. High-resolution MDCT allows for the assessment of trabecular bone microstructure at the spine. MDCT-based trabecular bone microstructure parameters and finite element models have shown to improve the prediction of bone strength beyond DXA-based BMD and revealed pharmacotherapy effects, which were partly not captured by BMD. Furthermore, recent studies demonstrated that quantitative magnetic resonance imaging (MRI) including proton single-voxel magnetic resonance spectroscopy (1H-MRS) and chemical shift-based water-fat imaging techniques quantifying bone marrow fat content at the spine may provide complementary information for diagnosing osteoporosis and assessing vertebral fracture risk.


Osteoporosis Vertebral fracture Dual-energy X-ray absorptiometry (DXA) Multi-detector computed tomography (MDCT) Magnetic resonance imaging (MRI) 


Conflict of Interest

The authors state no conflict of interest.


  1. 1.
    NIH (2001) NIH consensus development panel on osteoporosis prevention, diagnosis, and therapy, March 7–29, 2000: highlights of the conference. South Med J 94(6):569–573Google Scholar
  2. 2.
    Hallberg I, Bachrach-Lindstrom M, Hammerby S, Toss G, Ek AC (2009) Health-related quality of life after vertebral or hip fracture: a seven-year follow-up study. BMC Musculoskelet Disord 10:135Google Scholar
  3. 3.
    Papaioannou A, Kennedy CC, Ioannidis G, Sawka A, Hopman WM, Pickard L, Brown JP, Josse RG, Kaiser S, Anastassiades T, Goltzman D, Papadimitropoulos M, Tenenhouse A, Prior JC, Olszynski WP, Adachi JD (2009) The impact of incident fractures on health-related quality of life: 5 years of data from the Canadian Multicentre Osteoporosis Study. Osteoporos Int 20(5):703–714Google Scholar
  4. 4.
    Ioannidis G, Papaioannou A, Hopman WM, Akhtar-Danesh N, Anastassiades T, Pickard L, Kennedy CC, Prior JC, Olszynski WP, Davison KS, Goltzman D, Thabane L, Gafni A, Papadimitropoulos EA, Brown JP, Josse RG, Hanley DA, Adachi JD (2009) Relation between fractures and mortality: results from the Canadian Multicentre Osteoporosis Study. CMAJ 181(5):265–271Google Scholar
  5. 5.
    Jalava T, Sarna S, Pylkkanen L, Mawer B, Kanis JA, Selby P, Davies M, Adams J, Francis RM, Robinson J, McCloskey E (2003) Association between vertebral fracture and increased mortality in osteoporotic patients. J Bone Miner Res 18(7):1254–1260Google Scholar
  6. 6.
    Bliuc D, Nguyen ND, Milch VE, Nguyen TV, Eisman JA, Center JR (2009) Mortality risk associated with low-trauma osteoporotic fracture and subsequent fracture in men and women. JAMA 301(5):513–521Google Scholar
  7. 7.
    Svedbom A, Hernlund E, Ivergard M, Compston J, Cooper C, Stenmark J, McCloskey EV, Jonsson B, Kanis JA (2013) Osteoporosis in the European Union: a compendium of country-specific reports. Arch Osteoporos 8(1–2):137Google Scholar
  8. 8.
    Cauley JA (2013) Public health impact of osteoporosis. J Gerontol A Biol Sci Med Sci 68(10):1243–1251Google Scholar
  9. 9.
    Burge R, Dawson-Hughes B, Solomon DH, Wong JB, King A, Tosteson A (2007) Incidence and economic burden of osteoporosis-related fractures in the United States, 2005–2025. J Bone Miner Res 22(3):465–475Google Scholar
  10. 10.
    WHO Study Group (1994) Assessment of fracture risk and its application to screening for postmenopausal osteoporosis. Report of a WHO study group, World Health Organization technical report series, pp 843:1–129Google Scholar
  11. 11.
    Schuit SC, van der Klift M, Weel AE, de Laet CE, Burger H, Seeman E, Hofman A, Uitterlinden AG, van Leeuwen JP, Pols HA (2004) Fracture incidence and association with bone mineral density in elderly men and women: the Rotterdam study. Bone 34(1):195–202Google Scholar
  12. 12.
    Siris ES, Chen YT, Abbott TA, Barrett-Connor E, Miller PD, Wehren LE, Berger ML (2004) Bone mineral density thresholds for pharmacological intervention to prevent fractures. Arch Intern Med 164(10):1108–1112Google Scholar
  13. 13.
    Hopkins RB, Goeree R, Pullenayegum E, Adachi JD, Papaioannou A, Xie F, Thabane L (2011) The relative efficacy of nine osteoporosis medications for reducing the rate of fractures in post-menopausal women. BMC Musculoskelet Disord 12:209Google Scholar
  14. 14.
    Eriksen EF, Diez-Perez A, Boonen S (2013) Update on long-term treatment with bisphosphonates for postmenopausal osteoporosis: a systematic review. Bone 58C:126–135Google Scholar
  15. 15.
    Kanis JA, Oden A, Johansson H, Borgstrom F, Strom O, McCloskey E (2009) FRAX and its applications to clinical practice. Bone 44(5):734–743Google Scholar
  16. 16.
    Hillier TA, Cauley JA, Rizzo JH, Pedula KL, Ensrud KE, Bauer DC, Lui LY, Vesco KK, Black DM, Donaldson MG, Leblanc ES, Cummings SR (2011) WHO absolute fracture risk models (FRAX): do clinical risk factors improve fracture prediction in older women without osteoporosis? J Bone Miner Res 26(8):1774–1782Google Scholar
  17. 17.
    Delmas PD, Munoz F, Black DM, Cosman F, Boonen S, Watts NB, Kendler D, Eriksen EF, Mesenbrink PG, Eastell R (2009) Effects of yearly zoledronic acid 5 mg on bone turnover markers and relation of PINP with fracture reduction in postmenopausal women with osteoporosis. J Bone Miner Res 24(9):1544–1551Google Scholar
  18. 18.
    Lee J, Vasikaran S (2012) Current recommendations for laboratory testing and use of bone turnover markers in management of osteoporosis. Ann Lab Med 32(2):105–112Google Scholar
  19. 19.
    Lindsay R, Silverman SL, Cooper C, Hanley DA, Barton I, Broy SB, Licata A, Benhamou L, Geusens P, Flowers K, Stracke H, Seeman E (2001) Risk of new vertebral fracture in the year following a fracture. JAMA 285(3):320–323Google Scholar
  20. 20.
    Melton LJ III, Atkinson EJ, Cooper C, O’Fallon WM, Riggs BL (1999) Vertebral fractures predict subsequent fractures. Osteoporos Int 10(3):214–221Google Scholar
  21. 21.
    Roux C, Fechtenbaum J, Kolta S, Briot K, Girard M (2007) Mild prevalent and incident vertebral fractures are risk factors for new fractures. Osteoporos Int 18(12):1617–1624Google Scholar
  22. 22.
    van der Klift M, de Laet CE, McCloskey EV, Hofman A, Pols HA (2002) The incidence of vertebral fractures in men and women: the Rotterdam Study. J Bone Miner Res 17(6):1051–1056Google Scholar
  23. 23.
    Oei L, Rivadeneira F, Ly F, Breda SJ, Zillikens MC, Hofman A, Uitterlinden AG, Krestin GP, Oei EH (2013) Review of radiological scoring methods of osteoporotic vertebral fractures for clinical and research settings. Eur Radiol 23(2):476–486Google Scholar
  24. 24.
    Eastell R, Cedel SL, Wahner HW, Riggs BL, Melton LJ III (1991) Classification of vertebral fractures. J Bone Miner Res 6(3):207–215Google Scholar
  25. 25.
    McCloskey EV, Spector TD, Eyres KS, Fern ED, O’Rourke N, Vasikaran S, Kanis JA (1993) The assessment of vertebral deformity: a method for use in population studies and clinical trials. Osteoporos Int 3(3):138–147Google Scholar
  26. 26.
    Sone T, Tomomitsu T, Miyake M, Takeda N, Fukunaga M (1997) Age-related changes in vertebral height ratios and vertebral fracture. Osteoporos Int 7(2):113–118Google Scholar
  27. 27.
    Genant HK, Wu CY, van KC, Nevitt MC (1993) Vertebral fracture assessment using a semiquantitative technique. J Bone Miner Res 8(9):1137–1148Google Scholar
  28. 28.
    Jiang G, Eastell R, Barrington NA, Ferrar L (2004) Comparison of methods for the visual identification of prevalent vertebral fracture in osteoporosis. Osteoporos Int 15(11):887–896Google Scholar
  29. 29.
    Damilakis J, Adams JE, Guglielmi G, Link TM (2010) Radiation exposure in X-ray-based imaging techniques used in osteoporosis. Eur Radiol 20(11):2707–2714Google Scholar
  30. 30.
    Vokes T, Bachman D, Baim S, Binkley N, Broy S, Ferrar L, Lewiecki EM, Richmond B, Schousboe J (2006) Vertebral fracture assessment: the 2005 ISCD official positions. J Clin Densitom 9(1):37–46Google Scholar
  31. 31.
    Lewiecki EM (2010) Bone densitometry and vertebral fracture assessment. Curr Osteoporos Rep 8(3):123–130Google Scholar
  32. 32.
    Schousboe JT, Debold CR (2006) Reliability and accuracy of vertebral fracture assessment with densitometry compared to radiography in clinical practice. Osteoporos Int 17(2):281–289Google Scholar
  33. 33.
    Buehring B, Krueger D, Checovich M, Gemar D, Vallarta-Ast N, Genant HK, Binkley N (2010) Vertebral fracture assessment: impact of instrument and reader. Osteoporos Int 21(3):487–494Google Scholar
  34. 34.
    Guglielmi G, Muscarella S, Bazzocchi A (2011) Integrated imaging approach to osteoporosis: state-of-the-art review and update. Radiographics 31(5):1343–1364Google Scholar
  35. 35.
    Bauer JS, Muller D, Ambekar A, Dobritz M, Matsuura M, Eckstein F, Rummeny EJ, Link TM (2006) Detection of osteoporotic vertebral fractures using multidetector CT. Osteoporos Int 17(4):608–615Google Scholar
  36. 36.
    Muller D, Bauer JS, Zeile M, Rummeny EJ, Link TM (2008) Significance of sagittal reformations in routine thoracic and abdominal multislice CT studies for detecting osteoporotic fractures and other spine abnormalities. Eur Radiol 18(8):1696–1702Google Scholar
  37. 37.
    Delmas PD, van de Langerijt L, Watts NB, Eastell R, Genant H, Grauer A, Cahall DL (2005) Underdiagnosis of vertebral fractures is a worldwide problem: the IMPACT study. J Bone Miner Res 20(4):557–563Google Scholar
  38. 38.
    Fechtenbaum J, Cropet C, Kolta S, Verdoncq B, Orcel P, Roux C (2005) Reporting of vertebral fractures on spine X-rays. Osteoporos Int 16(12):1823–1826Google Scholar
  39. 39.
    Gehlbach SH, Bigelow C, Heimisdottir M, May S, Walker M, Kirkwood JR (2000) Recognition of vertebral fracture in a clinical setting. Osteoporos Int 11(7):577–582Google Scholar
  40. 40.
    Williams AL, Al-Busaidi A, Sparrow PJ, Adams JE, Whitehouse RW (2009) Under-reporting of osteoporotic vertebral fractures on computed tomography. Eur J Radiol 69(1):179–183Google Scholar
  41. 41.
    Gruber M, Dinges J, Muller D, Baum T, Rummeny EJ, Bauer J (2013) Impact of Specific Training in Detecting Osteoporotic Vertebral Fractures on Routine Chest Radiographs. Rofo 185(11):1074–1080Google Scholar
  42. 42.
    Roberts MG, Oh T, Pacheco EM, Mohankumar R, Cootes TF, Adams JE (2012) Semi-automatic determination of detailed vertebral shape from lumbar radiographs using active appearance models. Osteoporos Int 23(2):655–664Google Scholar
  43. 43.
    Baum T, Bauer JS, Netsch T, Klinder T, Dobritz M, Rummeny EJ, Noël PB, Lorenz C (2013) Automatic detection of osteoporotic vertebral fractures in routine thoracic and abdominal MDCT. Eur Radiol (epub ahead of print). doi: 10.1007/s00330-013-3089-2
  44. 44.
    Roberts MG, Pacheco EM, Mohankumar R, Cootes TF, Adams JE (2010) Detection of vertebral fractures in DXA VFA images using statistical models of appearance and a semi-automatic segmentation. Osteoporos Int 21(12):2037–2046Google Scholar
  45. 45.
    Blake GM, Fogelman I (2010) An update on dual-energy x-ray absorptiometry. Semin Nucl Med 40(1):62–73Google Scholar
  46. 46.
    Adams JE (2009) Quantitative computed tomography. Eur J Radiol 71(3):415–424Google Scholar
  47. 47.
    Felsenberg D, Gowin W (1999) Bone densitometry by dual energy methods. Radiologe 39(3):186–193Google Scholar
  48. 48.
    Adams JE (2013) Advances in bone imaging for osteoporosis. Nat Rev Endocrinol 9(1):28–42Google Scholar
  49. 49.
    Pickhardt PJ, Pooler BD, Lauder T, del Rio AM, Bruce RJ, Binkley N (2013) Opportunistic screening for osteoporosis using abdominal computed tomography scans obtained for other indications. Ann Intern Med 158(8):588–595Google Scholar
  50. 50.
    Guise TA (2006) Bone loss and fracture risk associated with cancer therapy. Oncologist 11(10):1121–1131Google Scholar
  51. 51.
    Bauer JS, Henning TD, Mueller D, Lu Y, Majumdar S, Link TM (2007) Volumetric quantitative CT of the spine and hip derived from contrast-enhanced MDCT: conversion factors. AJR Am J Roentgenol 188(5):1294–1301Google Scholar
  52. 52.
    Link TM, Koppers BB, Licht T, Bauer J, Lu Y, Rummeny EJ (2004) In vitro and in vivo spiral CT to determine bone mineral density: initial experience in patients at risk for osteoporosis. Radiology 231(3):805–811Google Scholar
  53. 53.
    Baum T, Muller D, Dobritz M, Rummeny EJ, Link TM, Bauer JS (2011) BMD measurements of the spine derived from sagittal reformations of contrast-enhanced MDCT without dedicated software. Eur J Radiol 80(2):e140–e145Google Scholar
  54. 54.
    Baum T, Muller D, Dobritz M, Wolf P, Rummeny EJ, Link TM, Bauer JS (2012) Converted lumbar BMD values derived from sagittal reformations of contrast-enhanced MDCT predict incidental osteoporotic vertebral fractures. Calcif Tissue Int 90(6):481–487Google Scholar
  55. 55.
    Acu K, Scheel M, Issever AS (2014) Time dependency of bone density estimation from computed tomography with intravenous contrast agent administration. Osteoporos Int 25(2):535–542Google Scholar
  56. 56.
    Summers RM, Baecher N, Yao J, Liu J, Pickhardt PJ, Choi JR, Hill S (2011) Feasibility of simultaneous computed tomographic colonography and fully automated bone mineral densitometry in a single examination. J Comput Assist Tomogr 35(2):212–216Google Scholar
  57. 57.
    Pickhardt PJ, Lee LJ, del Rio AM, Lauder T, Bruce RJ, Summers RM, Pooler BD, Binkley N (2011) Simultaneous screening for osteoporosis at CT colonography: bone mineral density assessment using MDCT attenuation techniques compared with the DXA reference standard. J Bone Miner Res 26(9):2194–2203Google Scholar
  58. 58.
    Baum T, Karampinos DC, Liebl H, Rummeny EJ, Waldt S, Bauer JS (2013) High-resolution bone imaging for osteoporosis diagnostics and therapy monitoring using clinical MDCT and MRI. Curr Med Chem 20(38):4844–4852Google Scholar
  59. 59.
    Link TM (2012) Osteoporosis imaging: state of the art and advanced imaging. Radiology 263(1):3–17MathSciNetGoogle Scholar
  60. 60.
    Burghardt AJ, Link TM, Majumdar S (2011) High-resolution computed tomography for clinical imaging of bone microarchitecture. Clin Orthop Relat Res 469(8):2179–2193Google Scholar
  61. 61.
    Krug R, Burghardt AJ, Majumdar S, Link TM (2010) High-resolution imaging techniques for the assessment of osteoporosis. Radiol Clin North Am 48(3):601–621Google Scholar
  62. 62.
    Baum T, Kutscher M, Muller D, Rath C, Eckstein F, Lochmuller EM, Rummeny EJ, Link TM, Bauer JS (2013) Cortical and trabecular bone structure analysis at the distal radius-prediction of biomechanical strength by DXA and MRI. J Bone Miner Metab 31(2):212–221Google Scholar
  63. 63.
    Ladinsky GA, Vasilic B, Popescu AM, Wald M, Zemel BS, Snyder PJ, Loh L, Song HK, Saha PK, Wright AC, Wehrli FW (2008) Trabecular structure quantified with the MRI-based virtual bone biopsy in postmenopausal women contributes to vertebral deformity burden independent of areal vertebral BMD. J Bone Miner Res 23(1):64–74Google Scholar
  64. 64.
    Krug R, Banerjee S, Han ET, Newitt DC, Link TM, Majumdar S (2005) Feasibility of in vivo structural analysis of high-resolution magnetic resonance images of the proximal femur. Osteoporos Int 16(11):1307–1314Google Scholar
  65. 65.
    Patsch JM, Burghardt AJ, Kazakia G, Majumdar S (2011) Noninvasive imaging of bone microarchitecture. Ann N Y Acad Sci 1240:77–87Google Scholar
  66. 66.
    Eckstein F, Lochmuller EM, Lill CA, Kuhn V, Schneider E, Delling G, Muller R (2002) Bone strength at clinically relevant sites displays substantial heterogeneity and is best predicted from site-specific bone densitometry. J Bone Miner Res 17(1):162–171Google Scholar
  67. 67.
    Ito M, Ikeda K, Nishiguchi M, Shindo H, Uetani M, Hosoi T, Orimo H (2005) Multi-detector row CT imaging of vertebral microstructure for evaluation of fracture risk. J Bone Miner Res 20(10):1828–1836Google Scholar
  68. 68.
    Bauer JS, Issever AS, Fischbeck M, Burghardt A, Eckstein F, Rummeny EJ, Majumdar S, Link TM (2004) Multislice-CT for structure analysis of trabecular bone—a comparison with micro-CT and biomechanical strength. Rofo 176(5):709–718Google Scholar
  69. 69.
    Baum T, Grabeldinger M, Rath C, Grande GE, Burgkart R, Patsch JM, Rummeny EJ, Link TM, Bauer JS (2014) Trabecular bone structure analysis of the spine using clinical MDCT: can it predict vertebral bone strength? J Bone Miner Metab 32(1):56–64Google Scholar
  70. 70.
    Bauer JS, Sidorenko I, Mueller D, Baum T, Issever AS, Eckstein F, Rummeny EJ, Link TM, Raeth CW (2014) Prediction of bone strength by muCT and MDCT-based finite-element-models: How much spatial resolution is needed? Eur J Radiol 83(1):e36–e42Google Scholar
  71. 71.
    Issever AS, Link TM, Kentenich M, Rogalla P, Burghardt AJ, Kazakia GJ, Majumdar S, Diederichs G (2010) Assessment of trabecular bone structure using MDCT: comparison of 64- and 320-slice CT using HR-pQCT as the reference standard. Eur Radiol 20(2):458–468Google Scholar
  72. 72.
    Klinder T, Wolz R, Lorenz C, Franz A, Ostermann J (2008) Spine segmentation using articulated shape models. Med Image Comput Comput Assist Interv 11(Pt 1):227–234Google Scholar
  73. 73.
    Klinder T, Ostermann J, Ehm M, Franz A, Kneser R, Lorenz C (2009) Automated model-based vertebra detection, identification, and segmentation in CT images. Med Image Anal 13(3):471–482Google Scholar
  74. 74.
    Ma J, Lu L, Zhan Y, Zhou X, Salganicoff M, Krishnan A (2010) Hierarchical segmentation and identification of thoracic vertebra using learning-based edge detection and coarse-to-fine deformable model. Med Image Comput Comput Assist Interv 13(Pt 1):19–27Google Scholar
  75. 75.
    Mastmeyer A, Engelke K, Fuchs C, Kalender WA (2006) A hierarchical 3D segmentation method and the definition of vertebral body coordinate systems for QCT of the lumbar spine. Med Image Anal 10(4):560–577Google Scholar
  76. 76.
    Shen H, Litvin A, Alvino C (2008) Localized priors for the precise segmentation of individual vertebras from CT volume data. Med Image Comput Comput Assist Interv 11(Pt 1):367–375Google Scholar
  77. 77.
    Parfitt AM, Drezner MK, Glorieux FH, Kanis JA, Malluche H, Meunier PJ, Ott SM, Recker RR (1987) Bone histomorphometry: standardization of nomenclature, symbols, and units. Report of the ASBMR Histomorphometry Nomenclature Committee. J Bone Miner Res 2(6):595–610Google Scholar
  78. 78.
    Räth C, Monetti R, Bauer J, Sidorenko I, Müller D, Matsuura M, Lochmüller EM, Zysset P, Eckstein F (2008) Strength through structure: visualization and local assessment of the trabecular bone structure. New J Phys 10(125010)Google Scholar
  79. 79.
    Sidorenko I, Monetti R, Bauer J, Mueller D, Rummeny E, Eckstein F, Matsuura M, Lochmueller EM, Zysset P, Raeth C (2011) Assessing methods for characterising local and global structural and biomechanical properties of the trabecular bone network. Curr Med Chem 18(22):3402–3409Google Scholar
  80. 80.
    Augat P, Schorlemmer S (2006) The role of cortical bone and its microstructure in bone strength. Age Ageing 35(Suppl 2):ii27–ii31Google Scholar
  81. 81.
    Carpenter RD (2013) Finite element analysis of the hip and spine based on quantitative computed tomography. Curr Osteoporos Rep 11(2):156–162Google Scholar
  82. 82.
    Dall’Ara E, Schmidt R, Pahr D, Varga P, Chevalier Y, Patsch J, Kainberger F, Zysset P (2010) 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 43(12):2374–2380Google Scholar
  83. 83.
    Dall’Ara E, Pahr D, Varga P, Kainberger F, Zysset P (2012) QCT-based finite element models predict human vertebral strength in vitro significantly better than simulated DEXA. Osteoporos Int 23(2):563–572Google Scholar
  84. 84.
    Imai K, Ohnishi I, Bessho M, Nakamura K (2006) Nonlinear finite element model predicts vertebral bone strength and fracture site. Spine (Phila Pa 1976) 31(16):1789–1794Google Scholar
  85. 85.
    Imai K, Ohnishi I, Yamamoto S, Nakamura K (2008) In vivo assessment of lumbar vertebral strength in elderly women using computed tomography-based nonlinear finite element model. Spine (Phila Pa 1976) 33(1):27–32Google Scholar
  86. 86.
    Wang X, Sanyal A, Cawthon PM, Palermo L, Jekir M, Christensen J, Ensrud KE, Cummings SR, Orwoll E, Black DM, Keaveny TM (2012) Prediction of new clinical vertebral fractures in elderly men using finite element analysis of CT scans. J Bone Miner Res 27(4):808–816Google Scholar
  87. 87.
    Graeff C, Timm W, Nickelsen TN, Farrerons J, Marin F, Barker C, Gluer CC (2007) Monitoring teriparatide-associated changes in vertebral microstructure by high-resolution CT in vivo: results from the EUROFORS study. J Bone Miner Res 22(9):1426–1433Google Scholar
  88. 88.
    Chevalier Y, Quek E, Borah B, Gross G, Stewart J, Lang T, Zysset P (2010) 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 46(1):41–48Google Scholar
  89. 89.
    Imai K, Ohnishi I, Matsumoto T, Yamamoto S, Nakamura K (2009) 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 20(5):801–810Google Scholar
  90. 90.
    Keaveny TM, Donley DW, Hoffmann PF, Mitlak BH, Glass EV, San Martin JA (2007) 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 22(1):149–157Google Scholar
  91. 91.
    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 50(1):200–208Google Scholar
  92. 92.
    Duque G (2008) Bone and fat connection in aging bone. Curr Opin Rheumatol 20(4):429–434Google Scholar
  93. 93.
    Bredella MA, Torriani M, Ghomi RH, Thomas BJ, Brick DJ, Gerweck AV, Rosen CJ, Klibanski A, Miller KK (2011) Vertebral bone marrow fat is positively associated with visceral fat and inversely associated with IGF-1 in obese women. Obesity (Silver Spring) 19(1):49–53Google Scholar
  94. 94.
    Gilsanz V, Chalfant J, Mo AO, Lee DC, Dorey FJ, Mittelman SD (2009) Reciprocal relations of subcutaneous and visceral fat to bone structure and strength. J Clin Endocrinol Metab 94(9):3387–3393Google Scholar
  95. 95.
    von Muhlen D, Safii S, Jassal SK, Svartberg J, Barrett-Connor E (2007) Associations between the metabolic syndrome and bone health in older men and women: the Rancho Bernardo Study. Osteoporos Int 18(10):1337–1344Google Scholar
  96. 96.
    Albala C, Yanez M, Devoto E, Sostin C, Zeballos L, Santos JL (1996) Obesity as a protective factor for postmenopausal osteoporosis. Int J Obes Relat Metab Disord 20(11):1027–1032Google Scholar
  97. 97.
    Fazeli PK, Horowitz MC, MacDougald OA, Scheller EL, Rodeheffer MS, Rosen CJ, Klibanski A (2013) Marrow fat and bone–new perspectives. J Clin Endocrinol Metab 98(3):935–945Google Scholar
  98. 98.
    Sheu Y, Cauley JA (2011) The role of bone marrow and visceral fat on bone metabolism. Curr Osteoporos Rep 9(2):67–75Google Scholar
  99. 99.
    Ma J (2008) Dixon techniques for water and fat imaging. J Magn Reson Imaging 28(3):543–558Google Scholar
  100. 100.
    Reeder SB, Pineda AR, Wen Z, Shimakawa A, Yu H, Brittain JH, Gold GE, Beaulieu CH, Pelc NJ (2005) Iterative decomposition of water and fat with echo asymmetry and least-squares estimation (IDEAL): application with fast spin-echo imaging. Magn Reson Med 54(3):636–644Google Scholar
  101. 101.
    Shen W, Gong X, Weiss J, Jin Y (2013) Comparison among T1-weighted magnetic resonance imaging, modified dixon method, and magnetic resonance spectroscopy in measuring bone marrow fat. J Obes 2013:298675Google Scholar
  102. 102.
    Griffith JF, Yeung DK, Antonio GE, Lee FK, Hong AW, Wong SY, Lau EM, Leung PC (2005) Vertebral bone mineral density, marrow perfusion, and fat content in healthy men and men with osteoporosis: dynamic contrast-enhanced MR imaging and MR spectroscopy. Radiology 236(3):945–951Google Scholar
  103. 103.
    Griffith JF, Yeung DK, Antonio GE, Wong SY, Kwok TC, Woo J, Leung PC (2006) Vertebral marrow fat content and diffusion and perfusion indexes in women with varying bone density: MR evaluation. Radiology 241(3):831–838Google Scholar
  104. 104.
    Li X, Kuo D, Schafer AL, Porzig A, Link TM, Black D, Schwartz AV (2011) Quantification of vertebral bone marrow fat content using 3 Tesla MR spectroscopy: reproducibility, vertebral variation, and applications in osteoporosis. J Magn Reson Imaging 33(4):974–979Google Scholar
  105. 105.
    Shen W, Scherzer R, Gantz M, Chen J, Punyanitya M, Lewis CE, Grunfeld C (2012) Relationship between MRI-measured bone marrow adipose tissue and hip and spine bone mineral density in African-American and Caucasian participants: the CARDIA study. J Clin Endocrinol Metab 97(4):1337–1346Google Scholar
  106. 106.
    Shen W, Chen J, Punyanitya M, Shapses S, Heshka S, Heymsfield SB (2007) MRI-measured bone marrow adipose tissue is inversely related to DXA-measured bone mineral in Caucasian women. Osteoporos Int 18(5):641–647Google Scholar
  107. 107.
    Kuhn JP, Hernando D, Meffert PJ, Reeder S, Hosten N, Laqua R, Steveling A, Ender S, Schroder H, Pillich DT (2013) Proton-density fat fraction and simultaneous R2* estimation as an MRI tool for assessment of osteoporosis. Eur Radiol 23(12):3432–3439Google Scholar
  108. 108.
    Yu H, Shimakawa A, McKenzie CA, Brodsky E, Brittain JH, Reeder SB (2008) Multiecho water-fat separation and simultaneous R2* estimation with multifrequency fat spectrum modeling. Magn Reson Med 60(5):1122–1134Google Scholar
  109. 109.
    Liu CY, McKenzie CA, Yu H, Brittain JH, Reeder SB (2007) Fat quantification with IDEAL gradient echo imaging: correction of bias from T(1) and noise. Magn Reson Med 58(2):354–364Google Scholar
  110. 110.
    Reeder SB, Hu HH, Sirlin CB (2012) Proton density fat-fraction: a standardized MR-based biomarker of tissue fat concentration. J Magn Reson Imaging 36(5):1011–1014Google Scholar
  111. 111.
    Karampinos DC, Melkus G, Baum T, Bauer JS, Rummeny EJ, Krug R (2013) Bone marrow fat quantification in the presence of trabecular bone: initial comparison between water-fat imaging and single-voxel MRS. Magn Reson Med (Epub ahead of print). doi: 10.1002/mrm.24775
  112. 112.
    Baum T, Yap SP, Karampinos DC, Nardo L, Kuo D, Burghardt AJ, Masharani UB, Schwartz AV, Li X, Link TM (2012) Does vertebral bone marrow fat content correlate with abdominal adipose tissue, lumbar spine bone mineral density, and blood biomarkers in women with type 2 diabetes mellitus? J Magn Reson Imaging 35(1):117–124Google Scholar
  113. 113.
    Patsch JM, Li X, Baum T, Yap SP, Karampinos DC, Schwartz AV, Link TM (2013) Bone marrow fat composition as a novel imaging biomarker in postmenopausal women with prevalent fragility fractures. J Bone Miner Res 28(8):1721–1728Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Thomas Baum
    • 1
  • Dimitrios C. Karampinos
    • 1
  • Stefan Ruschke
    • 1
  • Hans Liebl
    • 1
  • Peter B. Noël
    • 1
  • Jan S. Bauer
    • 2
  1. 1.Department of Radiology, Klinikum rechts der IsarTechnische Universität MünchenMunichGermany
  2. 2.Section of Neuroradiology, Klinikum rechts der IsarTechnische Universität MünchenMunichGermany

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