Current Treatment Options in Rheumatology

, Volume 4, Issue 2, pp 133–141 | Cite as

Novel Imaging Modalities in Osteoporosis Diagnosis and Risk Stratification

  • Saarah Haque
  • Arthur Lau
  • Karen Beattie
  • Jonathan D. Adachi
Osteoporosis (A Lau, Section Editor)
Part of the following topical collections:
  1. Topical Collection on Osteoporosis


Purpose of review

Two hundred million individuals worldwide are diagnosed with osteoporosis, and every year, approximately 8.9 million experience a fracture. There is an opportunity with new diagnostic technology to enhance risk stratification of osteoporosis to improve patient outcomes. The current standard for osteoporosis diagnosis includes an areal bone mineral density (aBMD) T-score derived from a dual-energy X-ray absorptiometry (DXA) scan. However, aBMD does not account for bone quality, resulting in some individuals at risk for fracture not being identified. This review article will explore the potential of novel imaging technologies in osteoporosis diagnosis and risk stratification.

Recent findings

Several novel imaging technologies have had success identifying those at risk for fracture and measuring treatment effectiveness. These include trabecular bone score (TBS), high-resolution peripheral quantitative computed tomography (HR-pQCT), peripheral quantitative computed tomography (pQCT), magnetic resonance imaging (MRI), and quantitative ultrasound (QUS). Recently, TBS has been incorporated into fracture risk prediction.


While these imaging modalities show promise, further investigation is necessary to determine accuracy and reliability in osteoporosis diagnostics and fracture risk stratification before clinical integration is possible.


Osteoporosis Fracture Imaging Peripheral quantitative tomography Magnetic resonance 


Compliance with Ethical Standards

Conflict of Interest

The authors declare that they have no conflict of interest.

Human and Animal Rights and Informed Consent

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

References and Recommended Reading

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

  1. 1.
    Johnell O, Kanis JA. An estimate of the worldwide prevalence and disability associated with osteoporotic fractures. Osteoporos Int. 2006;17:1726–33.CrossRefPubMedGoogle Scholar
  2. 2.
    Anonymous. Consensus development conference: diagnosis, prophylaxis and treatment of osteoporosis. Am J Med. 1993;94:646–50.CrossRefGoogle Scholar
  3. 3.
    Wallace I, Rubin C, Lieberman D. Osteoporosis. Evol Med Public Health. 2015;2015(1):343.CrossRefPubMedPubMedCentralGoogle Scholar
  4. 4.
    Shuid A, Khaithir T, Mokhtar S, Mohamed I. A systematic review of the outcomes of osteoporotic fracture patients after hospital discharge: morbidity, subsequent fractures, and mortality. Ther Clin Risk Manag. 2014;10:937–48.CrossRefGoogle Scholar
  5. 5.
    Celi M, Rao C, Scialdoni A, Tempesta V, Gasbarra E, Pistillo P, et al. Bone mineral density evaluation in osteoporosis: why yes and why not? Aging Clin Exp Res. 2013;25(S1):47–9.CrossRefGoogle Scholar
  6. 6.
    Kanis J, Johnell O, Oden A, Dawson A, De Laet C, Jonsson B. Ten year probabilities of osteoporotic fractures according to BMD and diagnostic thresholds. Osteoporos Int. 2001;12:989–95.CrossRefPubMedGoogle Scholar
  7. 7.
    Siris ES, Chen YT, Abbott TA, Barrett-Connor E, Miller PD, Wehren LE, et al. Bone mineral density thresholds for pharmacological intervention to prevent fractures. Arch Intern Med. 2004;164:1108–12.CrossRefPubMedGoogle Scholar
  8. 8.
    Cosman F, de Beur SJ, LeBoff MS, Lewiecki EM, Tanner B, Randall S, et al. Clinicians guide to prevention and treatment of osteoporosis. Osteoporos Int. 2014;25:2359–81.CrossRefPubMedPubMedCentralGoogle Scholar
  9. 9.
    Didier H, Barthe N, Boutroy S, Pothuaud L, Winzenrieth R, Krieg M. 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. 2011;14(3):302–12.CrossRefGoogle Scholar
  10. 10.
    Winzenrieth R, Piveteau T, Hans D. Assessment of correlations between 3D μCT microarchitecture parameters and TBS: effects of resolution and correlation with TBS DXA measurements. J Clin Densitom. 2011;14(2):169.Google Scholar
  11. 11.
    Harvey N, Glüer C, Binkley N, McCloskey E, Brandi M, Cooper C, et al. Trabecular bone score (TBS) as a new complementary approach for osteoporosis evaluation in clinical practice. Bone. 2015;78:216–24.CrossRefPubMedPubMedCentralGoogle Scholar
  12. 12.
    Hans D, Goertzen AL, Krieg M-A, Leslie WD. Bone micro-architecture assessed by TBS predicts osteoporotic fractures independent of bone density: the Manitoba study. J Bone Miner Res. 2011;26:2762–9.CrossRefPubMedGoogle Scholar
  13. 13.
    Kanis JA, Oden A, Harvey NC, Leslie WD, Hans D, Johansson H, et al. A meta-analysis of trabecular bone score in fracture risk prediction and its interaction with FRAX. Osteoporos Int. 2015;26:940–8.Google Scholar
  14. 14.
    Tjong W, Kazakia GJ, Burghardt AJ, Majumdar S. The effect of voxel size on high-resolution peripheral computed tomography measurements of trabecular and cortical bone microstructure. Med Phys. 2012;39:1893–903.CrossRefPubMedPubMedCentralGoogle Scholar
  15. 15.
    MacNeil JA, Boyd SK. Improved reproducibility of highresolution peripheral quantitative computed tomography for measurement of bone quality. Med Eng Phys. 2008;30:792–9.CrossRefPubMedGoogle Scholar
  16. 16.
    Boutroy S, Bouxsein ML, Munoz F, Delmas PD. In vivo assessment of trabecular bone microarchitecture by high-resolution peripheral quantitative computed tomography. J Clin Endocrinol Metab. 2005;90(12):6508–15.CrossRefPubMedGoogle Scholar
  17. 17.
    Burghardt AJ, Buie HR, Laib A, Majumdar S, Boyd SK. Reproducibility of direct quantitative measures of cortical bone microarchitecture of the distal radius and tibia by HR-pQCT. Bone. 2010;47:519–28.CrossRefPubMedPubMedCentralGoogle Scholar
  18. 18.
    Nishiyama K, Shane E. Clinical imaging of bone microarchitecture with HR-pQCT. Curr Osteoporos Rep. 2013;11(2):147–55.CrossRefPubMedPubMedCentralGoogle Scholar
  19. 19.
    Liu X, Cohen A, Shane E, Yin P, Stein E, Rogers H, et al. Bone density, geometry, microstructure, and stiffness: relationships between peripheral and central skeletal sites assessed by DXA, HR-pQCT, and cQCT in premenopausal women. J Bone Miner Res. 2010;25(10):2229–38.CrossRefPubMedPubMedCentralGoogle Scholar
  20. 20.
    •• Wong A. A comparison of peripheral imaging technologies for bone and muscle quantification: a mixed methods clinical review. Curr Osteoporos Rep. 2016;14(6):359–73. This article provides a comprehensive analysis and consolidation of the literature on novel imaging technology including peripheral quantitative tomography and magnetic resonance imaging.CrossRefPubMedGoogle Scholar
  21. 21.
    Walker MD, McMahon DJ, Udesky J, Liu G, Bilezikian JP. Application of high resolution skeletal imaging to measurements of volumetric bone density and skeletal microarchitecture in Chinese American and Caucasian women: explanation of a paradox. J Bone Miner Res. 2009;24(12):1953–9.CrossRefPubMedPubMedCentralGoogle Scholar
  22. 22.
    • Cheung A, Adachi J, Hanley D, Kendler D, Davison K, Josse R, et al. High-resolution peripheral quantitative computed tomography for the assessment of bone strength and structure: a review by the Canadian Bone Strength Working Group. Curr Osteoporos Rep. 2013;11(2):136–46. This article provides a thorough overview of high-resolution peripheral quantitative tomography for predictive fracture risk assessment and risk stratification in patients with osteoporosis.CrossRefPubMedPubMedCentralGoogle Scholar
  23. 23.
    Hansen S, Hauge EM, Jensen JE, Brixen K. 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. 2012;10:736–45.Google Scholar
  24. 24.
    Wong AKO, Berger C, Ioannidis G, Beattie KA, Gordon CL, Pickard L, et al. The Canadian Multicentre Osteoporosis Bone Quality Study (CaMos BQS): baseline comparison of HR-pQCT and pQCT and fracture associations. J Bone Miner Res. 2015;30(Suppl 1):#P251.Google Scholar
  25. 25.
    Jones E, Bishop P, Woods A, Green J. Cross-sectional area and muscular strength. Sports Med. 2008;38(12):987–94.CrossRefPubMedGoogle Scholar
  26. 26.
    Wong AKO, Beattie KA, Min KKH, Gordon C, Pickard L, Papaioannou A, et al. Peripheral quantitative computed tomography-derived muscle density and peripheral magnetic resonance imaging-derived muscle adiposity: precision and associations with fragility fractures in women. J Musculoskelet Neuronal Interact. 2014;14(40):401–10.PubMedPubMedCentralGoogle Scholar
  27. 27.
    Wong A, Hummel K, Moore C, Beattie K, Shaker S, Craven B, et al. Improving reliability of pQCT-derived muscle area and density measures using a watershed algorithm for muscle and fat segmentation. J Clin Densitometry. 2015;18(1):93–101.CrossRefGoogle Scholar
  28. 28.
    Zebaze RM, Ghasem-Zadeh A, Bohte A, Iuliano-Burns S, Mirams M, Price RI, et al. Intracortical remodelling and porosity in the distal radius and post-mortem femurs of women: a cross-sectional study. Lancet. 2010;375:1729–36.CrossRefPubMedGoogle Scholar
  29. 29.
    Link T. Osteoporosis imaging: state of the art and advanced imaging. Radiology. 2012;263(1):3–17.CrossRefPubMedPubMedCentralGoogle Scholar
  30. 30.
    Dennison EM, Jameson KA, Edwards MH, Denison HJ, Aihie Sayer A, Cooper C. Peripheral quantitative computed tomography measures are associated with adult fracture risk: the Hertfordshire Cohort Study. Bone. 2014;64:13–7.CrossRefPubMedGoogle Scholar
  31. 31.
    Burt L, Liang Z, Sajobi T, Hanley D, Boyd S. Sex- and site-specific normative data curves for HR-pQCT. J Bone Miner Res. 2016;31(11):2041–7.CrossRefPubMedGoogle Scholar
  32. 32.
    Hung V, Zhu T, Cheung W, Fong T, Yu F, Hung L, et al. Age-related differences in volumetric bone mineral density, microarchitecture, and bone strength of distal radius and tibia in Chinese women: a high-resolution pQCT reference database study. Osteoporos Int. 2015;26(6):1691–703.CrossRefPubMedGoogle Scholar
  33. 33.
    Jiang H, Yates C, Gorelik A, Kale A, Song Q, Wark J. Peripheral quantitative computed tomography measures contribute to the understanding of bone fragility in low-trauma fracture patients. Bone Abstracts. 2016.
  34. 34.
    Krug R, Banerjee S, Han ET, Newitt DC, Link TM, Majumdar S. Feasibility of in vivo structural analysis of high-resolution magnetic resonance images of the proximal femur. Osteoporos Int. 2005;16:1307–14.CrossRefPubMedGoogle Scholar
  35. 35.
    Hotca A, Rajapakse CS, Cheng C, Honig S, Egol K, Regatte RR, et al. In vivo measurement reproducibility of femoral neck microarchitectural parameters derived from 3T MR images. J Magn Reson Imaging. 2015;42:1339–45.CrossRefPubMedPubMedCentralGoogle Scholar
  36. 36.
    Zhang N, Magland JF, Rajapakse CS, Bhagat YA, Wehrli FW. Potential of in vivo MRI-based nonlinear finite-element analysis for the assessment of trabecular bone post-yield properties. Med Phys. 2013;40:1–10.CrossRefGoogle Scholar
  37. 37.
    SornayRendu E, Boutroy S, Munoz F, Delmas PD. Alterations of cortical and trabecular architecture are associated with fractures in postmenopausal women, partially independent of decreased BMD measured by DXA: the OFELY study. J Bone Miner Res. 2007;22:425–33.CrossRefGoogle Scholar
  38. 38.
    Chang G, Rajapakse CS, Regatte RR, Babb J, Saxena A, Belmont HM, et al. 3 tesla MRI detects deterioration in proximal femur microarchitecture and strength in long-term glucocorticoid users compared with controls. J Magn Reson Img. 2015;42:1489–96.CrossRefGoogle Scholar
  39. 39.
    Folkesson J, Goldenstein J, Carballido-Gamio J, Kazakia G, Burghardt AJ, Rodriguez A, et al. Longitudinal evaluation of the effects of alendronate on MRI bone microarchitecture in postmenopausal osteopenic women. Bone. 2011;48:611–21.CrossRefPubMedGoogle Scholar
  40. 40.
    Wang X, Shen X, Li X, Agrawal CM. Age-related changes in the collagen network and toughness of bone. Bone. 2002;31:1–7.CrossRefPubMedGoogle Scholar
  41. 41.
    VanRietbergen B, Majumdar S, Newitt D, MacDonald B. High-resolution MRI and micro-FE for the evaluation of changes in bone mechanical properties during longitudinal clinical trials: application to calcaneal bone in postmenopausal women after one year of idoxifene treatment. Clin Biomech. 2002;17:81–8.CrossRefGoogle Scholar
  42. 42.
    Seeman E, Delmas PD, Hanley DA, Sellmeyer D, Cheung AM, Shane E, et al. Microarchitectural deterioration of cortical and trabecular bone: Differing effects of denosumab and alendronate. J Bone Miner Res. 2010 Aug;25(8):1886–94.CrossRefPubMedPubMedCentralGoogle Scholar
  43. 43.
    Lam S, Wald M, Rajapakse C, Liu Y, Saha P, Wehrli F. Performance of the MRI-based virtual bone biopsy in the distal radius: serial reproducibility and reliability of structural and mechanical parameters in women representative of osteoporosis study populations. Bone. 2011;49(4):895–903.CrossRefPubMedPubMedCentralGoogle Scholar
  44. 44.
    Gregg E, Kriska A, Salamone L, Roberts MM, Anderson SJ, Ferrell RE, et al. The epidemiology of quantitative ultrasound: a review of the relationships with bone mass, osteoporosis and fracture risk. Osteoporos Int. 1997;7:89–99.CrossRefPubMedGoogle Scholar
  45. 45.
    Guglielmi G, Terlizzi FD. Quantitative ultrasound in the assessment of osteoporosis. Eur J Radiol. 2009;71:425–31.CrossRefPubMedGoogle Scholar
  46. 46.
    Bouxsein ML, Coan BS, Lee SC. Prediction of the strength of the elderly proximal femur by bone mineral density and quantitative ultrasound measurements of the heel and tibia. Bone. 1999;25:49–54.CrossRefPubMedGoogle Scholar
  47. 47.
    Moayyeri A, Adams JE, Adler RA, Krieg MA, Hans D, Compston, et al. Quantitative ultrasound of the heel and fracture risk assessment: an updated meta-analysis. Osteoporos Int. 2012;23:143–53.CrossRefPubMedGoogle Scholar
  48. 48.
    Chan MY, Nguyen ND, Center JR, Eisman JA, Nguyen TV. Absolute fracture-risk prediction by a combination of calcaneal quantitative ultrasound and bone mineral density. Calcif Tissue Int. 2012;90:128–36.CrossRefPubMedGoogle Scholar
  49. 49.
    Villa P, Lassandro A, Moruzzi M, Amar ID, Vacca L, Nardo D, et al. A non-invasive prevention program model for the assessment of osteoporosis in the early postmenopausal period: a pilot study on FRAX and QUS tools advantages. J Endocrinol Investig. 2016;39:191–8.CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Saarah Haque
    • 1
  • Arthur Lau
    • 2
  • Karen Beattie
    • 2
  • Jonathan D. Adachi
    • 2
  1. 1.OakvilleCanada
  2. 2.HamiltonCanada

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