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Advances in bone imaging for osteoporosis

Nature Reviews Endocrinology volume 9, pages 28–42 (2013)Cite this article

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Abstract

The diagnosis and management of osteoporosis have been improved by the development of new quantitative methods of skeletal assessment and by the availability of an increasing number of therapeutic options, respectively. A number of imaging methods exist and all have advantages and disadvantages. Dual-energy X-ray absorptiometry (DXA) is the most widely available and commonly utilized method for clinical diagnosis of osteoporosis and will remain so for the foreseeable future. The WHO 10-year fracture risk assessment tool (FRAX®) will improve clinical use of DXA and the cost-effectiveness of therapeutic intervention. Improved reporting of radiographic features that suggest osteoporosis and the presence of vertebral fracture, which are powerful predictors of future fractures, could increase the frequency of appropriate DXA referrals. Quantitative CT remains predominantly a research tool, but has advantages over DXA—allowing measurement of volumetric density, separate measures of cortical and trabecular bone density, and evaluation of bone shape and size. High resolution imaging, using both CT and MRI, has been introduced to measure trabecular and cortical bone microstructure. Although these methods provide detailed insights into the effects of disease and therapies on bone, they are technically challenging and not widely available, so they are unlikely to be used in clinical practice.

Key Points

  • Osteoporotic fractures cause substantial morbidity, mortality, and societal and economic effects; therefore, radiographic and other imaging features indicating osteoporosis should be reported, and patients referred for DXA or other investigations

  • Imaging methods are available to provide quantitative assessment of the skeleton to identify patients with osteoporosis; this assessment should ideally take place before insufficiency fractures occur

  • Dual energy X-ray absorptiometry (DXA) is the most widely available and most frequently utilized clinical quantitative bone-imaging technique

  • Areal BMD accounts for approximately 60–70% of bone strength and its measurement by DXA is used as a surrogate for bone strength in fracture risk prediction

  • Primarily a research tool, quantitative CT (QCT) has some advantages over DXA but involves higher doses of ionising radiation than DXA when applied to central sites including spine and hip

  • High-resolution techniques image the cortical and trabecular microstructure; these research tools are technically challenging and not widely available but will improve our understanding of osteoporosis and the effects of pharmacotherapy

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Figure 1: Parameters that contribute to bone strength.
Figure 2: Radiographic and other bone imaging techniques can provide clues or evidence of osteoporosis, and such evidence should be clearly reported and further investigation, such as use of DXA, suggested.
Figure 3: Radiogrammetry is an established method in which a radiograph of the nondominant hand is used to measure the mid-shaft width of the cortex of the second metacarpal and to express this length as a ratio of the diameter of the bone in the same site—the 'metacarpal index'.
Figure 4: DXA is the most widely available and utilized quantitative bone densitometry method used to diagnose osteoporosis in clinical practice.
Figure 5: DXA is size-dependent.
Figure 6: QCT has advantages over DXA.
Figure 7: Imaging bone microstructure in vivo.

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References

  1. van Staa, T. P., Dennison, E. M., Leufkens, H. G. & Cooper, C. Epidemiology of fractures in England and Wales. Bone 29, 517–522 (2001).

    Article  CAS  PubMed  Google Scholar 

  2. [No authors listed] Osteoporosis prevention, diagnosis, and therapy. NIH Consensus Development Panel on Osteoporosis Prevention, Diagnosis, and Therapy. JAMA 285, 785–795 (2001).

  3. Turner, C. H. Bone strength: current concepts. Ann. NY Acad. Sci. 1068, 429–446 (2006).

    Article  PubMed  Google Scholar 

  4. Bouxsein, M. L. Bone quality: where do we go from here? Osteoporos. Int. 14 (Suppl. 5), S118–S127 (2003).

    Article  PubMed  Google Scholar 

  5. Cummings, S. R. & Melton, L. J. Epidemiology and outcomes of osteoporotic fractures. Lancet 359, 1761–1767 (2002).

    Article  PubMed  Google Scholar 

  6. Compston, J. Osteoporosis: social and economic impact. Radiol. Clin. North Am. 48, 477–482 (2010).

    Article  PubMed  Google Scholar 

  7. Guglielmi, G., Muscarella, S. & Bazzocchi, A. Integrated imaging approach to osteoporosis: state-of-the-art review and update. Radiographics 31, 1343–1364 (2011).

    Article  PubMed  Google Scholar 

  8. Link, T. M. The Founder's Lecture 2009: advances in imaging of osteoporosis and osteoarthritis. Skeletal Radiol. 39, 943–955 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  9. Anil, G., Guglielmi, G. & Peh, W. C. Radiology of osteoporosis. Radiol. Clin. North Am. 48, 497–518 (2010).

    Article  PubMed  Google Scholar 

  10. Melton, L. J. 3rd, Atkinson, E. J., Cooper, C., O'Fallon, W. M. & Riggs, B. L. Vertebral fractures predict subsequent fractures. Osteoporos. Int. 10, 214–221 (1999).

    Article  PubMed  Google Scholar 

  11. Black, D. M., Arden, N. K., Palermo, L., Pearson, J. & Cummings, S. R. Prevalent vertebral deformities predict hip fractures and new vertebral deformities but not wrist fractures. Study of Osteoporotic Fractures Research Group. J. Bone Miner. Res. 14, 821–828 (1999).

    Article  CAS  PubMed  Google Scholar 

  12. Lindsay, R. et al. Risk of new vertebral fracture in the year following a fracture. JAMA 285, 320–323 (2001).

    Article  CAS  PubMed  Google Scholar 

  13. Link, T. M., Guglielmi, G., van Kuijk, C. & Adams, J. E. Radiologic assessment of osteoporotic vertebral fractures: diagnostic and prognostic implications. Eur. Radiol. 15, 1521–1532 (2005).

    Article  PubMed  Google Scholar 

  14. Bauer, J. S. et al. Detection of osteoporotic vertebral fractures using multidetector CT. Osteoporos. Int. 17, 608–615 (2006).

    Article  CAS  PubMed  Google Scholar 

  15. Müller, D., Bauer, J. S., Zeile, M., Rummeny, E. J. & Link, T. M. Significance of sagittal reformations in routine thoracic and abdominal multislice CT studies for detecting osteoporotic fractures and other spine abnormalities. Eur. Radiol. 18, 1696–1702 (2008).

    Article  PubMed  Google Scholar 

  16. Williams, A. L., Al-Busaidi, A., Sparrow, P. J., Adams, J. E. & Whitehouse, R. W. Under-reporting of osteoporotic vertebral fractures on computed tomography. Eur. J. Radiol. 69, 179–183 (2009).

    Article  PubMed  Google Scholar 

  17. Gehlbach, S. H. et al. Recognition of vertebral fracture in a clinical setting. Osteoporos. Int. 11, 577–582 (2000).

    Article  CAS  PubMed  Google Scholar 

  18. Delmas, P. D. et al. Underdiagnosis of vertebral fractures is a worldwide problem: the IMPACT study. Bone Miner. Res. 20, 557–563 (2005).

    Article  Google Scholar 

  19. International Osteoporosis Foundation. Vertebral Fracture Initiative 2010 [online], (2010).

  20. Barnett, E. & Nordin, B. E. The radiological diagnosis of osteoporosis: a new approach. Clin. Radiol. 11, 166–174 (1960).

    Article  CAS  PubMed  Google Scholar 

  21. Virtama, P. & Mahonen, H. Thickness of the cortical layer as an estimate of mineral content of human finger bones. Br. J. Radiol. 33, 60–62 (1960).

    Article  CAS  PubMed  Google Scholar 

  22. Adams, J. E. Radiogrammetry and radiographic absorptiometry. Radiol. Clin. North Am. 48, 531–540 (2010).

    Article  PubMed  Google Scholar 

  23. Adams, P., Davies, G. T. & Sweetnam, P. M. Observer error and measurements of the metacarpal. Br. J. Radiol. 42, 192–197 (1969).

    Article  CAS  PubMed  Google Scholar 

  24. Cootes, T., Hill, A., Taylor, C. J. & Haslam, J. Use of active shape models for locating structure in medical images. Image Vision Comput. 12, 355–365 (1994).

    Article  Google Scholar 

  25. Jørgensen J. T., Andersen, P. B, Rosholm, A. & Bjarnason, N. H. Digital X-ray radiogrammetry: a new appendicular bone densitometric method with high precision. Clin. Physiol. 20, 330–335 (2000).

    Article  PubMed  Google Scholar 

  26. Rosholm, A., Hyldstrup, L., Backsgaard, L., Grunkin, M. & Thodberg H. H. Estimation of bone mineral density by digital X-ray radiogrammetry: theoretical background and clinical testing. Osteoporos. Int. 12, 961–969 (2001).

    Article  CAS  PubMed  Google Scholar 

  27. Hoff, M. et al. Short-time in vitro and in vivo precision of direct digital X-ray radiogrammetry. J. Clin. Densitom. 12, 17–21 (2009).

    Article  PubMed  Google Scholar 

  28. Dhainaut, A. et al. Long-term in-vitro precision of direct digital X-ray radiogrammetry. Skeletal Radiol. 40, 1575–1579 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  29. Pfeil, A. et al. Value of digital X-ray radiogrammetry in the assessment of inflammatory bone loss in rheumatoid arthritis. Arthritis Care Res. (Hoboken) 63, 666–674 (2011).

    Article  Google Scholar 

  30. Pye, S. R. et al. Disease activity and severity in early inflammatory arthritis predict hand cortical bone loss. Rheumatology (Oxford) 49, 1943–1948 (2010).

    Article  Google Scholar 

  31. Bouxsein, M. L., Palermo, L., Yeung, C. & Black, D. M. Digital X-ray radiogrammetry predicts hip, wrist and vertebral fracture risk in elderly women: a prospective analysis from the study of osteoporotic fractures. Osteoporos. Int. 13, 358–365 (2000).

    Article  Google Scholar 

  32. Bach-Mortensen, P. et al. Digital x-ray radiogrammetry identifies women at risk of osteoporotic fracture: results from a prospective study. Calcif. Tissue Int. 79, 1–6 (2006).

    Article  CAS  PubMed  Google Scholar 

  33. Böttcher, J. et al. Normative data for digital X-ray radiogrammetry from a female and male German cohort. J. Clin. Densitom. 9, 341–350 (2009).

    Article  Google Scholar 

  34. Black, D. M. et al. A normative reference database study for Pronosco X-posure System. J. Clin. Densitom. 4, 5–12 (2001).

    Article  CAS  PubMed  Google Scholar 

  35. Cameron, J. R. & Sorenson, J. Measurement of bone mineral density in vivo: an improved method. Science 142, 230–232 (1963).

    Article  CAS  PubMed  Google Scholar 

  36. Mazess, R. B. & Barden, H. S. Measurement of bone by dual-photon absorptiometry (DPA) and dual-energy X-ray absorptiometry (DEXA). Ann. Chir. Gynaecol. 77, 197–203 (1988).

    CAS  PubMed  Google Scholar 

  37. Cullum, I. D., Ell, P. J. & Ryder, J. P. X-ray dual photon absorptiometry: a new method for the measurement of bone density. Brit. J. Radiol. 62, 587–592 (1989).

    Article  CAS  PubMed  Google Scholar 

  38. Mazess, R. B. Bone densitometry of the axial skeleton. Orthop. Clin. North Am. 21, 51–63 (1990).

    CAS  PubMed  Google Scholar 

  39. Blake, G. M. & Fogelman, I. The clinical role of dual energy X-ray absorptiometry. Eur. J. Radiol. 71, 406–414 (2009).

    Article  PubMed  Google Scholar 

  40. Blake, G. M. & Fogelman, I. An update on dual-energy x-ray absorptiometry. Semin. Nucl. Med. 40, 62–73 (2010).

    Article  PubMed  Google Scholar 

  41. Marshall, D., Johnell, O. & Wedel, H. Meta-analysis of how well measures of bone density predict occurrence of osteoporotic fractures. Br. Med. J. 312, 1254–1259 (1996).

    Article  CAS  Google Scholar 

  42. Adams, J. E. & Bishop, N. in Dual Energy X-ray Absorptiometry (DXA) in Adults and Children 7th edn Ch. 29 (ed. Rosen, C.) 152–158 (American Society for Bone and Mineral Research, Washington DC, 2009).

    Google Scholar 

  43. Damilakis, J., Adams, J. E., Guglielmi, G. & Link, T. M. Radiation exposure in X-ray-based imaging techniques used in osteoporosis. Eur. Radiol. 20, 2707–2714 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  44. Gluer, C. C., Blake, G., Blunt, B. A., Jergas, M. & Genant, H. K. Accurate assessment of precision errors: how to measure the reproducibility of bone densitometry techniques. Osteoporos. Int. 5, 262–270 (1995).

    Article  CAS  PubMed  Google Scholar 

  45. Gluer, C. C. Monitoring skeletal changes by radiological techniques. J. Bone Miner. Res. 14, 1952–1962 (1999).

    Article  CAS  PubMed  Google Scholar 

  46. Compston, J. Monitoring osteoporosis treatment. Best Pract. Res. Clin. Rheumatol. 23, 781–788 (2009).

    Article  PubMed  Google Scholar 

  47. Lewiecki, E. M. Benefits and limitations of bone mineral density and bone turnover markers to monitor patients treated for osteoporosis. Curr. Osteoporos. Rep. 8, 15–22 (2010).

    Article  PubMed  Google Scholar 

  48. Kalender, W. A. et al. The European Spine Phantom—a tool for standardization and quality control in spinal bone mineral measurements by DXA and QCT. Eur. J. Radiol. 20, 83–92 (1995).

    Article  CAS  PubMed  Google Scholar 

  49. Bachrach, L. K. Assessing bone health in children: who to test and what does it mean? Pediatr. Endocrinol. Rev. 2 (Suppl. 3), 332–336 (2005).

    PubMed  Google Scholar 

  50. Wren, T. A. & Gilsanz, V. Assessing bone mass in children and adolescents. Curr. Osteoporos. Rep. 4, 153–158 (2006).

    Article  PubMed  Google Scholar 

  51. Baim, S. et al. Official positions of the International Society for Clinical Densitometry and executive summary of the 2007 ISCD Pediatric Position Development Conference. J. Clin. Densitom. 11, 6–21 (2008).

    Article  PubMed  Google Scholar 

  52. van Rijn, R. R. & Van Kuijk, C. Of small bones and big mistakes; bone densitometry in children revisited. Eur. J. Radiol. 71, 432–439 (2009).

    Article  CAS  PubMed  Google Scholar 

  53. van Kuijk, C. Pediatric bone densitometry. Radiol. Clin. North Am. 48, 623–627 (2010).

    Article  PubMed  Google Scholar 

  54. Kanis, J. A. & Glüer, C. C. An update on the diagnosis and assessment of osteoporosis with densitometry. Committee of Scientific Advisors, International Osteoporosis Foundation. Osteoporos. Int. 11, 192–202 (2000).

    Article  CAS  PubMed  Google Scholar 

  55. Blake, G. M., Lewiecki, E. M., Kendler, D. L. & Fogelman, I. A review of strontium ranelate and its effect on DXA scans. J. Clin. Densitom. 10, 113–119 (2007).

    Article  PubMed  Google Scholar 

  56. Liao, J., Blake, G. M., McGregor, A. H. & Patel, R. The effect of bone strontium on BMD is different for different manufacturers' DXA systems. Bone 47, 882–887 (2010).

    Article  CAS  PubMed  Google Scholar 

  57. Blake, G. M., Herd, R. J. & Fogelman, I. A longitudinal study of supine lateral DXA of the lumbar spine: a comparison with postero-anterior spine, hip and total-body DXA. Osteoporos. Int. 6, 462–470 (1996).

    Article  CAS  PubMed  Google Scholar 

  58. Kanis, J. A. Diagnosis of osteoporosis and assessment of fracture risk. Lancet 359, 1929–1936 (2002).

    Article  PubMed  Google Scholar 

  59. Looker, A. C. et al. Updated data on proximal femur bone mineral levels of US adults. Osteoporos. Int. 8, 468–489 (1998).

    Article  CAS  PubMed  Google Scholar 

  60. Dasher, L. G, Newton, C. D. & Lenchik, L. Dual X-ray absorptiometry in today's clinical practice. Radiol. Clin. North Am. 48, 541–560 (2010).

    Article  PubMed  Google Scholar 

  61. Chun, K. J. Bone densitometry. Semin. Nucl. Med. 41, 220–228 (2011).

    Article  PubMed  Google Scholar 

  62. WHO Study Group. Assessment of fracture risk and its application to screening for postmenopausal osteoporosis. WHO, Geneva, Switzerland (WHO Technical Report Series 843, 1994).

  63. Blake, G. M. & Fogelman, I. The role of DXA bone density scans in the diagnosis and treatment of osteoporosis. Postgrad. Med. J. 83, 509–517 (2007).

    Article  PubMed  PubMed Central  Google Scholar 

  64. Kanis, J. A et al. The use of clinical risk factors enhances the performance of BMD in the prediction of osteoporotic fractures in men and women. Osteoporos. Int. 18, 1033–1046 (2007).

    Article  CAS  PubMed  Google Scholar 

  65. Kanis, J. A., Johansson, H., Oden, A. & McCloskey, E. V. Assessment of fracture risk. Eur. J. Radiol. 71, 392–397 (2009).

    Article  PubMed  Google Scholar 

  66. Johnell, O. et al. Predictive value of BMD for hip and other fractures. J. Bone Miner. Res. 20, 1185–1194 (2005).

    Article  PubMed  Google Scholar 

  67. Kanis, J. A. et al. Case finding for the management of osteoporosis with FRAX—assessment and intervention thresholds for the UK. Osteoporos. Int. 19, 1395–1408 (2008).

    Article  CAS  PubMed  Google Scholar 

  68. Compston, J. et al. Guidelines for the diagnosis and management of osteoporosis in postmenopausal women and men from the age of 50 years in the UK. Maturitas 62, 105–108 (2009).

    Article  CAS  PubMed  Google Scholar 

  69. Johansson, H., Kanis, J. A., Oden, A., Compston, J. & McCloskey, E. A comparison of case-finding strategies in the UK for the management of hip fractures. Osteoporos. Int. 23, 907–915 (2012).

    Article  CAS  PubMed  Google Scholar 

  70. Kanis, J. A. et al. Development and use of FRAX® in osteoporosis. Osteoporos. Int. 21 (Suppl. 2), S407–S413 (2010).

    Article  PubMed  Google Scholar 

  71. Binkley, N. & Lewiecki, E. M. The evolution of fracture risk estimation. J. Bone Miner. Res. 25, 2098–2100 (2010).

    Article  PubMed  Google Scholar 

  72. Lewiecki, E. M. et al. Official positions for FRAX® bone mineral density and FRAX® simplification from Joint Official Positions Development Conference of the International Society for Clinical Densitometry and International Osteoporosis Foundation on FRAX®. J. Clin. Densitom. 14, 226–236 (2011).

    Article  PubMed  Google Scholar 

  73. Compston, J. Management of glucocorticoid-induced osteoporosis. Nat. Rev. Rheumatol. 6, 82–88 (2010).

    Article  CAS  PubMed  Google Scholar 

  74. Reid, D. M. et al. Guidance for the management of breast cancer treatment-induced bone loss: a consensus position statement from a UK Expert Group. Cancer Treat. Rev. 34 (Suppl. 1), S3–S18 (2008).

    Article  CAS  Google Scholar 

  75. Gordon, C. M et al. Dual energy X-ray absorptiometry interpretation and reporting in children and adolescents: the 2007 ISCD Pediatric Official Positions. J. Clin. Densitom. 11, 43–58 (2008).

    Article  PubMed  Google Scholar 

  76. Bishop, N. et al. Dual-energy X-ray absorptiometry assessment in children and adolescents with diseases that may affect the skeleton: the 2007 pediatric official positions. J. Clin. Densitom. 11, 29–42 (2008).

    Article  PubMed  Google Scholar 

  77. Shepherd, J. A. et al. Optimal monitoring time interval between DXA measures in children. J. Bone Miner. Res. 26, 2745–2752 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  78. Ward, K. A., Ashby, R. L., Roberts, S. A., Adams, J. E. & Zulf Mughal, M. UK reference data for the Hologic QDR Discovery dual-energy X ray absorptiometry scanner in healthy children and young adults aged 6–17 years. Arch. Dis. Child. 92, 53–59 (2007).

    Article  PubMed  Google Scholar 

  79. Kalkwarf, H. J. et al. The bone mineral density in childhood study: bone mineral content and density according to age, sex, and race. J. Clin. Endocrinol. Metab. 92, 2087–2099 (2007).

    Article  CAS  PubMed  Google Scholar 

  80. Zemel, B. S. et al. Revised pediatric reference data for the lateral distal femur measured by Hologic Discovery/Delphi dual-energy X-ray absorptiometry. J. Clin. Densitom. 12, 207–218 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  81. Faulkner, K. G. et al. Simple measurement of femoral geometry predicts hip fracture: the study of osteoporotic fractures. J. Bone Miner. Res. 8, 1211–1217 (1993).

    Article  CAS  PubMed  Google Scholar 

  82. Faulkner, K. G, McClung, M. & Cummings, S. R. Automated evaluation of hip axis length for predicting hip fracture. J. Bone Miner. Res. 9, 1065–1070 (1994).

    Article  CAS  PubMed  Google Scholar 

  83. Beck, T. J. Extending DXA beyond bone mineral density: understanding hip structure analysis. Curr. Osteoporos. Rep. 5, 49–55 (2007).

    Article  PubMed  Google Scholar 

  84. Prevrhal, S. et al. Comparison of DXA hip structural analysis with volumetric QCT. J. Clin. Densitom. 11, 232–236 (2008).

    Article  PubMed  Google Scholar 

  85. Bouxsein, M. L. & Karasik, D. Bone geometry and skeletal fragility. Curr. Osteoporos. Rep. 4, 49–56 (2006).

    Article  PubMed  Google Scholar 

  86. Tuck, S. P. et al. Femoral neck shaft angle in men with fragility fractures. J. Osteoporos. 2011, 903726 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  87. Gregory, J. S. et al. A method for assessment of the shape of the proximal femur and its relationship to osteoporotic hip fracture. Osteoporos. Int. 15, 5–11 (2004).

    Article  CAS  PubMed  Google Scholar 

  88. Gregory, J. S., Stewart, A., Undrill, P. E., Reid, D. M. & Aspden, R. M. Bone shape, structure, and density as determinants of osteoporotic hip fracture: a pilot study investigating the combination of risk factors. Invest. Radiol. 40, 591–597 (2005).

    Article  PubMed  Google Scholar 

  89. Vokes, T. et al. Vertebral fracture assessment: the 2005 ISCD official positions. J. Clin. Densitom. 9, 37–46 (2006).

    Article  PubMed  Google Scholar 

  90. Lewiecki, E. M. Bone densitometry and vertebral fracture assessment. Curr. Osteoporos. Rep. 8, 123–130 (2010).

    Article  PubMed  Google Scholar 

  91. Mäyränpää, M. K. et al. Bone densitometry in the diagnosis of vertebral fractures in children: accuracy of vertebral fracture assessment. Bone 41, 353–359 (2007).

    Article  PubMed  Google Scholar 

  92. Schousboe, J. T. et al. Abdominal aortic calcification detected on lateral spine images from a bone densitometer predicts incident myocardial infarction or stroke in older women. J. Bone Miner. Res. 23, 409–416 (2008).

    Article  PubMed  Google Scholar 

  93. Hans, D. et al. Correlations between trabecular bone score, measured using anteroposterior dual-energy X-ray absorptiometry acquisition, and 3-dimensional parameters of bone microarchitecture: an experimental study on human cadaver vertebrae. J. Clin. Densitom. 14, 302–312 (2011).

    Article  PubMed  Google Scholar 

  94. Hans, D., Goertzen, A. L., Krieg, M. A. & Leslie, W. D. Bone microarchitecture assessed by TBS predicts osteoporotic fractures independent of bone density: the Manitoba study. J. Bone Miner. Res. 26, 2762–2769 (2011).

    Article  PubMed  Google Scholar 

  95. Bousson, V. et al. Trabecular bone score (TBS): available knowledge, clinical relevance, and future prospects. Osteoporos. Int. 23, 1489–1501 (2012).

    Article  CAS  PubMed  Google Scholar 

  96. Kelly, T. L., Wilson, K. E. & Heymsfield, S. B. Dual energy X-ray absorptiometry body composition reference values from NHANES. PLoS One 4, e7038 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  97. Frost, H. M. Bone's mechanostat: a 2003 update. Anat. Rec. A Discov. Mol. Cell. Evol. Biol. 275, 1081–1101 (2003).

    Article  PubMed  Google Scholar 

  98. Carter, D. R., Bouxsein, M. L. & Marcus, R. New approaches for interpreting projected bone densitometry data. J. Bone Miner. Res. 7, 137–145 (1992).

    Article  CAS  PubMed  Google Scholar 

  99. Kröger, H., Kotaniemi, A., Vainio, P. & Alhava, E. Bone densitometry of the spine and femur in children by dual-energy x-ray absorptiometry. Bone Miner. 17, 75–85 (1992).

    Article  PubMed  Google Scholar 

  100. Lu, P. W., Cowell, C. T., Lloyd-Jones, S. A., Briody, J. & Howman-Giles, R. Volumetric bone mineral density in normal subjects, aged 5–27 years. J. Clin. Endocrinol. Metab. 81, 1586–1590 (1996).

    CAS  PubMed  Google Scholar 

  101. Mølgaard, C., Thomsen, B. L., Prentice, A., Cole, T. J. & Michaelsen, K. F. Whole body bone mineral content in healthy children and adolescents. Arch. Dis. Child. 76, 9–15 (1997).

    Article  PubMed  PubMed Central  Google Scholar 

  102. Crabtree, N. J. et al. The relationship between lean body mass and bone mineral content in paediatric health and disease. Bone 35, 965–972 (2004).

    Article  CAS  PubMed  Google Scholar 

  103. Adams, J. E. Quantitative computed tomography. Eur. J. Radiol. 71, 415–424 (2009).

    Article  PubMed  Google Scholar 

  104. 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. Clin. Densitom. 11, 123–162 (2008).

    Article  Google Scholar 

  105. Bousson, V. D. et al. In vivo discrimination of hip fracture with quantitative computed tomography: results from the prospective European Femur Fracture Study (EFFECT). J. Bone Miner. Res. 26, 881–893 (2011).

    Article  PubMed  Google Scholar 

  106. Lenchik, L. et al. Measurement of trabecular bone mineral density in the thoracic spine using cardiac gated quantitative computed tomography. J. Comput. Assist. Tomogr. 28, 134–139 (2004).

    Article  PubMed  Google Scholar 

  107. Engelke, K. et al. Quantitative computed tomography (QCT) of the forearm using general purpose spiral whole-body CT scanners: accuracy, precision and comparison with dual-energy X-ray absorptiometry (DXA). Bone 45, 110–118 (2009).

    Article  PubMed  Google Scholar 

  108. Yu, W. et al. Spinal bone mineral assessment in postmenopausal women: a comparison between dual X-ray absorptiometry and quantitative computed tomography. Osteoporos. Int. 5, 433–439 (1995).

    Article  CAS  PubMed  Google Scholar 

  109. Schneider, P. et al. Multicenter German reference data base for peripheral quantitative computer tomography. Technol. Health Care 3, 69–73 (1995).

    Article  CAS  PubMed  Google Scholar 

  110. Zemel, B. et al. Peripheral quantitative computed tomography in children and adolescents: the 2007 ISCD pediatric official positions. J. Clin. Densitom. 11, 59–74 (2008).

    Article  PubMed  Google Scholar 

  111. Ashby, R. L. et al. A reference database for the Stratec XCT-2000 peripheral quantitative computed tomography (pQCT) scanner in healthy children and young adults aged 6–19 years. Osteoporos. Int. 20, 1337–1346 (2009).

    Article  CAS  PubMed  Google Scholar 

  112. Rauch, F., Bailey, D. A., Baxter-Jones, A., Mirwald, R. & Faulkner, R. The 'muscle-bone unit' during the pubertal growth spurt. Bone 34, 771–775 (2004).

    Article  PubMed  Google Scholar 

  113. Prevrhal, S., Engelke, K. & Kalender, W. A. Accuracy limits for the determination of cortical width and density: the influence of object size and CT imaging parameters. Phys. Med. Biol. 44, 751–764 (1999).

    Article  CAS  PubMed  Google Scholar 

  114. Marjanovic, E. J., Ward, K. A. & Adams, J. E. The impact of accurate positioning on measurements made by peripheral QCT in the distal radius. Osteoporos. Int. 20, 1207–1214 (2009).

    Article  CAS  PubMed  Google Scholar 

  115. Langton, C. M., Palmer, S. B. & Porter, R. W. The measurement of broadband ultrasonic attenuation in cancellous bone. Eng. Med. 13, 89–91 (1984).

    Article  CAS  PubMed  Google Scholar 

  116. Krieg, M. A. et al. Quantitative ultrasound in the management of osteoporosis: the 2007 ISCD official positions. J. Clin. Densitom. 11, 163–187 (2008).

    Article  PubMed  Google Scholar 

  117. Baroncelli, G. I. Quantitative ultrasound methods to assess bone mineral status in children: technical characteristics, performance, and clinical application. Pediatr. Res. 63, 220–228 (2008).

    Article  PubMed  Google Scholar 

  118. Moayyeri, A. et al. Quantitative ultrasound of the heel and fracture risk assessment: an updated meta-analysis. Osteoporos. Int. 23, 143–153 (2012).

    Article  CAS  PubMed  Google Scholar 

  119. Siris, E. S. et al. Bone mineral density thresholds for pharmacological intervention to prevent fractures. Arch. Intern. Med. 164, 1108–1112 (2004).

    Article  PubMed  Google Scholar 

  120. Wasnich, R. D. & Miller, P. D. Antifracture efficacy of antiresorptive agents are related to changes in bone density. J. Clin. Endocrinol. Metab. 85, 231–236 (2000).

    Article  CAS  PubMed  Google Scholar 

  121. Cummings, S. R., Bates, D. & Black, D. M. Clinical use of bone densitometry: scientific review. JAMA 288 1889–1897 (2002).

    Article  PubMed  Google Scholar 

  122. Eastell, R. et al. Relationship of early changes in bone resorption to the reduction in fracture risk with risedronate. J. Bone Miner. Res. 18, 1051–1056 (2003).

    Article  CAS  PubMed  Google Scholar 

  123. Bouxsein, M. L. Technology insight: noninvasive assessment of bone strength in osteoporosis. Nat. Clin. Pract. Rheumatol. 4, 310–318 (2008).

    Article  PubMed  Google Scholar 

  124. Bouxsein, M. L. & Seeman, E. Quantifying the material and structural determinants of bone strength. Best Pract. Res. Clin. Rheumatol. 23, 741–753 (2009).

    Article  PubMed  Google Scholar 

  125. Singh, M., Nagrath, A. R. & Maini, P. S. Changes in trabecular pattern of the upper end of the femur as an index of osteoporosis. J. Bone Joint Surg. (Am.) 52, 457–467 (1970).

    Article  CAS  Google Scholar 

  126. Smyth, P. P., Adams, J. E., Whitehouse, R. W. & Taylor, C. J. Application of computer texture analysis to the Singh Index. Br. J. Radiol. 70, 242–247 (1997).

    Article  CAS  PubMed  Google Scholar 

  127. Kolta, S. et al. Bone texture analysis of human femurs using a new device (BMA™) improves failure load prediction. Osteoporos. Int. 23, 1311–1316 (2012).

    Article  CAS  PubMed  Google Scholar 

  128. Chappard, D., Guggenbuhl, P., Legrand, E., Baslé, M. F. & Audran, M. Texture analysis of X-ray radiographs is correlated with bone histomorphometry. J. Bone Miner. Metab. 23, 24–29 (2005).

    Article  PubMed  Google Scholar 

  129. Guggenbuhl, P., Bodic, F., Hamel, L., Baslé, M. F. & Chappard, D. Texture analysis of X-ray radiographs of iliac bone is correlated with bone micro-CT. Osteoporos. Int. 17, 447–454 (2006).

    Article  CAS  PubMed  Google Scholar 

  130. Chappard, C. et al. Prediction of femoral fracture load: cross-sectional study of texture analysis and geometric measurements on plain radiographs versus bone mineral density. Radiology 255, 536–543 (2010).

    Article  PubMed  Google Scholar 

  131. Zerfass, P. et al. Integrated segmentation and analysis approach for QCT of the knee to determine subchondral bone mineral density and texture. IEEE Trans. Biomed. Eng. 59, 2449–2258 (2012).

    Article  CAS  PubMed  Google Scholar 

  132. Griffith, J. F. & Genant, H. K. Bone mass and architecture determination: state of the art. Best Pract. Res. Clin. Endocrinol. Metab. 22, 737–764 (2008).

    Article  PubMed  Google Scholar 

  133. Ito, M. Recent progress in bone imaging for osteoporosis research. J. Bone Miner. Metab. 29, 131–140 (2011).

    Article  PubMed  Google Scholar 

  134. Patsch, J. M., Burghardt, A. J., Kazakia, G. & Majumdar, S. Noninvasive imaging of bone microarchitecture. Ann. NY Acad. Sci. 1240, 77–87 (2011).

    Article  PubMed  Google Scholar 

  135. Mueller, T. L. et al. Regional, age and gender differences in architectural measures of bone quality and their correlation to bone mechanical competence in the human radius of an elderly population. Bone 45, 882–891 (2009).

    Article  PubMed  Google Scholar 

  136. Donnelly, E. Methods for assessing bone quality: a review. Clin. Orthop. Relat. Res. 469, 2128–2138 (2011).

    Article  PubMed  Google Scholar 

  137. Peyrin, F. Evaluation of bone scaffolds by micro-CT. Osteoporos. Int. 22, 2043–2048 (2011).

    Article  CAS  PubMed  Google Scholar 

  138. Keyak, J. H. & Falkinstein, Y. Comparison of in situ and in vitro CT scan-based finite element model predictions of proximal femoral fracture load. Med. Eng. Phys. 25, 781–787 (2003).

    Article  PubMed  Google Scholar 

  139. Keyak, J. H., Kaneko, T. S., Tehranzadeh, J. & Skinner, H. B. Predicting proximal femoral strength using structural engineering models. Clin. Orthop. Relat. Res. 437, 219–228 (2005).

    Article  Google Scholar 

  140. Keaveny, T. M. Biomechanical computed tomography-noninvasive bone strength analysis using clinical computed tomography scans. Ann. NY Acad. Sci. 1192, 57–65 (2010).

    Article  PubMed  Google Scholar 

  141. Graeff, C. et al. Monitoring teriparatide-associated changes in vertebral microstructure by high-resolution CT in vivo: results from the EUROFORS study. J. Bone Miner. Res. 22, 1426–1433 (2007).

    Article  CAS  PubMed  Google Scholar 

  142. Thomas, C. D. et al. Femoral neck trabecular bone: loss with aging and role in preventing fracture. J. Bone Miner. Res. 24, 1808–1818 (2009).

    Article  PubMed  Google Scholar 

  143. Poole, K. E. et al. Changing structure of the femoral neck across the adult female lifespan. J. Bone Miner. Res. 25, 482–491 (2010).

    Article  PubMed  Google Scholar 

  144. Burghardt, A. J., Link, T. M. & Majumdar, S. High-resolution computed tomography for clinical imaging of bone microarchitecture. Clin. Orthop. Relat. Res. 469, 2179–2193 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  145. Zemel, B. S. Quantitative computed tomography and computed tomography in children. Curr. Osteoporos. Rep. 9, 284–290 (2011).

    Article  PubMed  Google Scholar 

  146. Burghardt, A. J. et al. High-resolution peripheral quantitative computed tomographic imaging of cortical and trabecular bone microarchitecture in patients with type 2 diabetes mellitus. J. Clin. Endocrinol. Metab. 95, 5045–5055 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  147. Krug, R., Burghardt, A. J., Majumdar, S. & Link, T. M. High-resolution imaging techniques for the assessment of osteoporosis. Radiol. Clin. North Am. 48, 601–621 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  148. Kazakia, G. J. 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. 23, 463–474 (2008).

    Article  PubMed  Google Scholar 

  149. Griffith, J. F., Engelke, K. & Genant, H. K. Looking beyond bone mineral density: imaging assessment of bone quality. Ann. NY Acad. Sci. 1192, 45–56 (2010).

    Article  PubMed  Google Scholar 

  150. Wehrli, F. W. Structural and functional assessment of trabecular and cortical bone by micro magnetic resonance imaging. J. Magn. Reson. Imaging 25, 390–409 (2007).

    Article  PubMed  Google Scholar 

  151. Issever, A. S. et al. Local differences in the trabecular bone structure of the proximal femur depicted with high-spatial-resolution MR imaging and multisection CT. Acad. Radiol. 9, 1395–1406 (2002).

    Article  PubMed  Google Scholar 

  152. Bauer, J. S. & Link, T. M. Advances in osteoporosis imaging. Eur. J. Radiol. 71, 440–449 (2009).

    Article  PubMed  Google Scholar 

  153. Du, J. et al. Qualitative and quantitative ultrashort echo time (UTE) imaging of cortical bone. J. Magn. Reson. 207, 304–311 (2010).

    Article  CAS  PubMed  Google Scholar 

  154. Techawiboonwong, A., Song, H. K., Leonard, M. B. & Wehrli, F. W. Cortical bone water: in vivo quantification with ultrashort echo-time MR imaging. Radiology 248, 824–833 (2008).

    Article  PubMed  PubMed Central  Google Scholar 

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  1. Department of Radiology, Manchester Academic Health Science Centre (MAHSC), The Royal Infirmary and University of Manchester, The Royal Infirmary, Oxford Road, Manchester, M13 9WL, UK

    Judith E. Adams

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Adams, J. Advances in bone imaging for osteoporosis. Nat Rev Endocrinol 9, 28–42 (2013). https://doi.org/10.1038/nrendo.2012.217

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