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Computational Anatomy in the Study of Bone Structure

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Abstract

Osteoporosis is a major public health threat for millions of Americans with billions of dollars per year of national direct costs for osteoporotic fractures. Osteoporosis results in a decrease in overall bone mass and subsequent increase in the risk of bone fracture. Bone strength arises from the combination of bone size and shape, the distribution of bone mass throughout the structure, and the quality of the bone material. Advances in medical imaging have enabled a comprehensive assessment of bone structure through the analysis of high-resolution scans of relevant anatomical sites, eg, the proximal femur. However, conventional imaging analysis techniques use predefined regions of interest that do not take full advantage of such scans. Recently, computational anatomy, a set of imaging-based analysis algorithms, has emerged as a promising technique in studies of osteoporosis. Computational anatomy enables analyses that are not biased to one particular region and provide a more complete assessment of the whole structure. In this article, we review studies that have used computational anatomy to investigate the structure of the proximal femur in relation to age, fracture, osteoporotic treatment, and spaceflight effects.

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References

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

  1. Osteoporosis prevention, diagnosis, and therapy. NIH consensus statement. 2000;17:1–36.

    Google Scholar 

  2. Kanis JA, Oden A, Johnell O, et al. The components of excess mortality after hip fracture. Bone. 2003;32:468–73.

    Article  PubMed  CAS  Google Scholar 

  3. Foss NB, Kehlet H. Mortality analysis in hip fracture patients: implications for design of future outcome trials. Br J Anaesth. 2005;94:24–9.

    Article  PubMed  CAS  Google Scholar 

  4. Blake GM, Fogelman I. The role of DXA bone density scans in the diagnosis and treatment of osteoporosis. Postgrad Med J. 2007;83:509–17.

    Article  PubMed  Google Scholar 

  5. Genant HK, Engelke K, Prevrhal S. Advanced CT bone imaging in osteoporosis. Rheumatology. 2008;47 Suppl 4:iv9–iv16.

    Article  PubMed  Google Scholar 

  6. Majumdar S. Magnetic resonance imaging for osteoporosis. Skeletal Radiol. 2008;37:95–7.

    Article  PubMed  Google Scholar 

  7. Lang TF. Quantitative computed tomography. Radiol Clin North Am. 2010;48:589–600.

    Article  PubMed  Google Scholar 

  8. Keyak JH, Rossi SA, Jones KA, Skinner HB. Prediction of femoral fracture load using automated finite element modeling. J Biomech. 1998;31:125–33.

    Article  PubMed  CAS  Google Scholar 

  9. Keyak JH, Kaneko TS, Tehranzadeh J, Skinner HB. Predicting proximal femoral strength using structural engineering models. Clin Orthop Relat R. 2005:219–28.

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

    Article  PubMed  CAS  Google Scholar 

  11. Carballido-Gamio J, Majumdar S. Clinical utility of microarchitecture measurements of trabecular bone. Curr Osteoporos Rep. 2006;4:64–70.

    Article  PubMed  Google Scholar 

  12. Krug R, Banerjee S, Han ET, et al. Feasibility of in vivo structural analysis of high-resolution magnetic resonance images of the proximal femur. Osteoporos Int. 2005;16:1307–14.

    Article  PubMed  Google Scholar 

  13. Krug R, Burghardt AJ, Majumdar S, Link TM. High-resolution imaging techniques for the assessment of osteoporosis. Radiol Clin North Am. 2010;48:601–21.

    Article  PubMed  Google Scholar 

  14. 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  PubMed  CAS  Google Scholar 

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

    Article  PubMed  Google Scholar 

  16. Rajapakse CS, Leonard MB, Bhagat YA, et al. Micro-MR imaging-based computational biomechanics demonstrates reduction in cortical and trabecular bone strength after renal transplantation. Radiology. 2012;262:912–20.

    Article  PubMed  Google Scholar 

  17. Wehrli FW, Hopkins JA, Hwang SN, et al. Cross-sectional study of osteopenia with quantitative MR imaging and bone densitometry. Radiology. 2000;217:527–38.

    PubMed  CAS  Google Scholar 

  18. Thompson PM, Apostolova LG. Computational anatomical methods as applied to ageing and dementia. Br J Radiol. 2007;80(Spec No 2):S78–91.

    Article  PubMed  Google Scholar 

  19. Robbins S, Evans AC, Collins DL, Whitesides S. Tuning and comparing spatial normalization methods. Med Image Anal. 2004;8:311–23.

    Article  PubMed  Google Scholar 

  20. Davies RH, Twining CJ, Cootes TF, et al. A minimum description length approach to statistical shape modeling. IEEE Trans Med Imaging. 2002;21:525–37.

    Article  PubMed  Google Scholar 

  21. Gregory JS, Testi D, Stewart A, et al. A method for assessment of the shape of the proximal femur and its relationship to osteoporotic hip fracture. Osteoporos Int. 2004;15:5–11.

    Article  PubMed  CAS  Google Scholar 

  22. Gregory JS, Stewart A, Undrill PE, et al. Bone shape, structure, and density as determinants of osteoporotic hip fracture: a pilot study investigating the combination of risk factors. Invest Radiol. 2005;40:591–7.

    Article  PubMed  Google Scholar 

  23. Baker-LePain JC, Luker KR, Lynch JA, et al. Active shape modeling of the hip in the prediction of incident hip fracture. J Bone Miner Res. 2011;26:468–74.

    Article  PubMed  Google Scholar 

  24. Goodyear SR, Barr RJ, McCloskey E, et al. Can we improve the prediction of hip fracture by assessing bone structure using shape and appearance modeling? Bone. 2012;53:188–93.

    Article  PubMed  Google Scholar 

  25. Li W, Kezele I, Collins DL, et al. Voxel-based modeling and quantification of the proximal femur using inter-subject registration of quantitative CT images. Bone. 2007;41:888–95.

    Article  PubMed  Google Scholar 

  26. Li W, Kornak J, Harris T, et al. Identify fracture-critical regions inside the proximal femur using statistical parametric mapping. Bone. 2009;44:596–602.

    Article  PubMed  Google Scholar 

  27. Poole KE, Treece GM, Mayhew PM, et al. Cortical thickness mapping to identify focal osteoporosis in patients with hip fracture. PLoS One. 2012;7:e38466.

    Article  PubMed  CAS  Google Scholar 

  28. Carballido-Gamio J, Harnish R, Saeed I, et al. Geometry, density distribution and internal structure of the proximal femur in relation to age and hip fracture risk in women. Minneapolis, MN: ASBMR; 2012.

    Google Scholar 

  29. •• Carballido-Gamio J, Harnish R, Saeed I, et al. Proximal femoral density distribution and structure in relation to age and hip fracture risk in women. J Bone Miner Res. 2013;28:537–46. Article demonstrating the spatial relationship of vBMD in the proximal femur with incident hip fracture and aging in women.

    Article  PubMed  Google Scholar 

  30. Davatzikos C, Vaillant M, Resnick SM, et al. A computerized approach for morphological analysis of the corpus callosum. J Comput Assist Tomogr. 1996;20:88–97.

    Article  PubMed  CAS  Google Scholar 

  31. Johannesdottir F, Poole KE, Reeve J, et al. Distribution of cortical bone in the femoral neck and hip fracture: a prospective case–control analysis of 143 incident hip fractures: the AGES-REYKJAVIK Study. Bone. 2011;48:1268–76.

    Article  PubMed  Google Scholar 

  32. •• Poole KE, Treece GM, Ridgway GR, et al. Targeted regeneration of bone in the osteoporotic human femur. PLoS One. 2011;6:e16190. Article demonstrating localized osteoporosis treatment effects on cortical bone thickness in the proximal femur.

    Article  PubMed  CAS  Google Scholar 

  33. Bryan R, Nair PB, Taylor M. Use of a statistical model of the whole femur in a large scale, multi-model study of femoral neck fracture risk. J Biomech. 2009;42:2171–6.

    Article  PubMed  Google Scholar 

  34. Rueckert D, Frangi AF, Schnabel JA. Automatic construction of 3-D statistical deformation models of the brain using nonrigid registration. IEEE Trans Med Imaging. 2003;22:1014–25.

    Article  PubMed  Google Scholar 

  35. •• Nicolella DP. Development of a parametric finite element model of the proximal femur using statistical shape and density modeling. Comput Methods Biomech Biomed Eng. 2012;15:101–10. Article demonstrating the feasibility of parametric finite element modeling of the proximal femur.

    Article  Google Scholar 

  36. Keller TS. Predicting the compressive mechanical behavior of bone. J Biomech. 1994;27:1159–68.

    Article  PubMed  CAS  Google Scholar 

  37. Li W, Kornak J, Harris T, et al. Hip fracture risk estimation based on principal component analysis of QCT atlas: a preliminary study. SPIE Medl Imaging. 2009

  38. Li W, Kornak J, Harris TB, et al. Bone fracture risk estimation based on image similarity. Bone. 2009;45:560–7.

    Article  PubMed  Google Scholar 

  39. Fritscher K, Grunerbl A, Hanni M, et al. Trabecular bone analysis in CT and X-ray images of the proximal femur for the assessment of local bone quality. IEEE Trans Med Imaging. 2009;28:1560–75.

    Article  PubMed  Google Scholar 

  40. Schuler B, Fritscher KD, Kuhn V, et al. Assessment of the individual fracture risk of the proximal femur by using statistical appearance models. Med Phys. 2010;37:2560–71.

    Article  PubMed  Google Scholar 

  41. Leber S, Fritscher KD, Schmoelz W, Schubert R. Statistical model based analysis of bone mineral density of lumbar spine. Int J Comput Assist Radiol Surg. 2009;4:239–43.

    Article  PubMed  Google Scholar 

  42. Whitmarsh T, Fritscher KD, Humbert L, et al. A statistical model of shape and bone mineral density distribution of the proximal femur for fracture risk assessment. Med Image Comput Comput Assist Interv. 2011;14:393–400.

    PubMed  Google Scholar 

  43. Bredbenner TL, Potter R, Mason RL, et al. Investigation of statistical shape and density modeling as a discriminator for clinical fracture risk. Toronto, CA: ASBMR; 2010. p. S83.

    Google Scholar 

  44. Orwoll E, Blank JB, Barrett-Connor E, et al. Design and baseline characteristics of the osteoporotic fractures in men (MrOS) study–a large observational study of the determinants of fracture in older men. Contemp Clin Trials. 2005;26:569–85.

    Article  PubMed  Google Scholar 

  45. Bredbenner TL, Mason RL, Havill LM, et al. Investigating fracture risk classifiers based on statistical shape and density modeling and the MrOS data set. San Francisco, CA: ORS; 2012.

    Google Scholar 

  46. Whitmarsh T, Fritscher KD, Humbert L, et al. Hip fracture discrimination from dual-energy X-ray absorptiometry by statistical model registration. Bone. 2012;51:896–901.

    Article  PubMed  Google Scholar 

  47. Carballido-Gamio J, Folkesson J, Karampinos DC, et al. Generation of an atlas of the proximal femur and its application to trabecular bone analysis. Magn Reson Med. 2011;66:1181–91.

    Article  PubMed  Google Scholar 

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Conflict of Interest

J Carballido-Gamio declares that he has no conflicts of interest. DP Nicolella declares that he has no conflicts 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.

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Authors and Affiliations

  1. Department of Radiology and Biomedical Imaging, University of California, San Francisco, 185 Berry St, Suite 350, San Francisco, CA, 94107, USA

    Julio Carballido-Gamio

  2. Musculoskeletal Biomechanics Section, Southwest Research Institute, San Antonio, TX, 78238, USA

    Daniel P. Nicolella

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  1. Julio Carballido-Gamio
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  2. Daniel P. Nicolella
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Correspondence to Julio Carballido-Gamio.

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Carballido-Gamio, J., Nicolella, D.P. Computational Anatomy in the Study of Bone Structure. Curr Osteoporos Rep 11, 237–245 (2013). https://doi.org/10.1007/s11914-013-0148-1

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