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Quantitative vertebral morphometry based on parametric modeling of vertebral bodies in 3D

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Abstract

Summary

Quantitative vertebral morphometry (QVM) was performed by parametric modeling of vertebral bodies in three dimensions (3D).

Introduction

Identification of vertebral fractures in two dimensions is a challenging task due to the projective nature of radiographic images and variability in the vertebral shape. By generating detailed 3D anatomical images, computed tomography (CT) enables accurate measurement of vertebral deformations and fractures.

Methods

A detailed 3D representation of the vertebral body shape is obtained by automatically aligning a parametric 3D model to vertebral bodies in CT images. The parameters of the 3D model describe clinically meaningful morphometric vertebral body features, and QVM in 3D is performed by comparing the parameters to their statistical values. Thresholds and parameters that best discriminate between normal and fractured vertebral bodies are determined by applying statistical classification analysis.

Results

The proposed QVM in 3D was applied to 454 normal and 228 fractured vertebral bodies, yielding classification sensitivity of 92.5 % at 7.5 % specificity, with corresponding accuracy of 92.5 % and precision of 86.1 %. The 3D shape parameters that provided the best separation between normal and fractured vertebral bodies were the vertebral body height and the inclination and concavity of both vertebral endplates.

Conclusion

The described QVM in 3D is able to efficiently and objectively discriminate between normal and fractured vertebral bodies and identify morphological cases (wedge, (bi)concavity, or crush) and grades (1, 2, or 3) of vertebral body fractures. It may be therefore valuable for diagnosing and predicting vertebral fractures in patients who are at risk of osteoporosis.

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References

  1. Johnell O, Kanis J (2005) Epidemiology of osteoporotic fractures. Osteoporosis Int 16:S3–S7

    Article  Google Scholar 

  2. Ferrar L, Jiang G, Adams J, Eastell R (2005) Identification of vertebral fractures: an update. Osteoporos Int 16(7):717–728

    Article  PubMed  CAS  Google Scholar 

  3. Melton L, Atkinson E, Cooper C, O’Fallon W, Riggs B (1999) Vertebral fractures predict subsequent fractures. Osteoporos Int 10(3):214–221

    Article  PubMed  Google Scholar 

  4. 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):577–582

    Article  PubMed  CAS  Google Scholar 

  5. 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):557–563

    Article  PubMed  Google Scholar 

  6. Genant HK, Wu CY, van Kuijk C, Nevitt MC (1993) Vertebral fracture assessment using a semiquantitative technique. J Bone Miner Res 8(9):1137–1148

    Article  PubMed  CAS  Google Scholar 

  7. Schousboe JT, Vokes T, Broy SB, Ferrar L, McKiernan F, Roux C, Binkley N (2008) Vertebral fracture assessment: the 2007 ISCD official positions. J Clin Densitom 11(1):92–108

    Article  PubMed  Google Scholar 

  8. 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):887–896

    Article  PubMed  CAS  Google Scholar 

  9. Ferrar L, Jiang G, Schousboe JT, DeBold CR, Eastell R (2008) Algorithm-based qualitative and semiquantitative identification of prevalent vertebral fracture: agreement between different readers, imaging modalities, and diagnostic approaches. J Bone Miner Res 23(3):417–424

    Article  PubMed  Google Scholar 

  10. Minne H, Leidig G, Wuster C, Siromachkostov L, Baldauf G, Bickel R, Sauer P, Lojen M, Ziegler R (1988) A newly developed spine deformity index (SDI) to quantitate vertebral crush fractures in patients with osteoporosis. Bone Miner 3(4):335–349

    PubMed  CAS  Google Scholar 

  11. Melton L, Kan SH, Frye MA, Wahner HW, O’Fallon WM, Riggs BL (1989) Epidemiology of vertebral fractures in women. Am J Epidemiol 129(5):1000–1011

    PubMed  Google Scholar 

  12. Eastell R, Cedel SL, Wahner HW, Riggs BL, Melton LJ 3rd (1991) Classification of vertebral fractures. J Bone Miner Res 6(3):207–215

    Article  PubMed  CAS  Google Scholar 

  13. 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):138–147

    Article  PubMed  CAS  Google Scholar 

  14. Genant HK, Jergas M, Palermo L, Nevitt M, Valentin RS, Black D, Cummings SR (1996) Comparison of semiquantitative visual and quantitative morphometric assessment of prevalent and incident vertebral fractures in osteoporosis. J Bone Miner Res 11(7):984–996

    Article  PubMed  CAS  Google Scholar 

  15. Smyth PP, Taylor CJ, Adams JE (1999) Vertebral shape: automatic measurement with active shape models. Radiology 211(2):571–578

    PubMed  CAS  Google Scholar 

  16. de Bruijne M, Lund MT, Tankó LB, Pettersen PC, Nielsen M (2007) Quantitative vertebral morphometry using neighbor-conditional shape models. Med Image Anal 11(5):503–512

    Article  PubMed  Google Scholar 

  17. Brett A, Miller CG, Hayes CW, Krasnow J, Ozanian T, Abrams K, Block JE, van Kuijk C (2009) Development of a clinical workflow tool to enhance the detection of vertebral fractures: accuracy and precision evaluation. Spine 34(22):2437–2443

    Article  PubMed  Google Scholar 

  18. Roberts M, Cootes TF, Adams JE (2006) Vertebral morphometry—semiautomatic determination of detailed shape from dual-energy X-ray absorptiometry images using active appearance models. Invest Radiol 41(12):849–859

    Article  PubMed  Google Scholar 

  19. Roberts M, Cootes T, Pacheco E, Adams J (2007) Quantitative vertebral fracture detection on DXA images using shape and appearance models. Acad Radiol 14(10):1166–1178

    Article  PubMed  Google Scholar 

  20. Roberts M, Pacheco EMB, 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):2037–2046

    Article  PubMed  CAS  Google Scholar 

  21. Roberts M, Oh T, Pacheco EMB, 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):655–664

    Article  PubMed  CAS  Google Scholar 

  22. Kim YM, Demissie S, Eisenberg R, Samelson EJ, Kiel DP, Bouxsein ML (2011) Intra- and inter-reader reliability of semi-automated quantitative morphometry measurements and vertebral fracture assessment using lateral scout views from computed tomography. Osteoporos Int 22(10):2677–2688

    Article  PubMed  CAS  Google Scholar 

  23. Kim YM, Demissie S, Genant HK, Cheng X, Yu W, Samelson EJ, Kiel DP, Bouxsein ML (2012) Identification of prevalent vertebral fractures using CT lateral scout views: a comparison of semi-automated quantitative vertebral morphometry and radiologist semi-quantitative grading. Osteoporos Int 23(3):1007–1016

    Article  PubMed  CAS  Google Scholar 

  24. Ito Z, Harada A, Matsui Y, Takemura M, Wakao N, Suzuki T, Nihashi T, Kawatsu S, Shimokata H, Ishiguro N (2006) Can you diagnose for vertebral fracture correctly by plain X-ray? Osteoporos Int 17(11):1584–1591

    Article  PubMed  CAS  Google Scholar 

  25. Kolta S, Quiligotti S, Ruyssen-Witrand A, Amido A, Mitton D, Bras AL, Skalli W, Roux C (2008) In vivo 3D reconstruction of human vertebrae with the three-dimensional X-ray absorptiometry (3D-XA) method. Osteoporos Int 19(2):185–192

    Article  PubMed  CAS  Google Scholar 

  26. Kolta S, Kerkeni S, Travert C, Skalli W, Eastell R, Glüer CC, Roux C (2012) Variations in vertebral body dimensions in women measured by 3D-XA: a longitudinal in vivo study. Bone 50(3):777–783

    Article  PubMed  CAS  Google Scholar 

  27. Woo EK, Mansoubi H, Alyas F (2008) Incidental vertebral fractures on multidetector CT images of the chest: prevalence and recognition. Clin Radiol 63(2):160–164

    Article  PubMed  CAS  Google Scholar 

  28. 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):179–183

    Article  PubMed  Google Scholar 

  29. Parizel PM, Zijden T, Gaudino S, Spaepen M, Voormolen MHJ, Venstermans C, Belder F, den Hauwe L, Goethem JV (2009) Trauma of the spine and spinal cord: imaging strategies. Eur Spine J 19(S1):8–17

    Article  Google Scholar 

  30. Štern D, Likar B, Pernuš F, Vrtovec T (2011) Parametric modelling and segmentation of vertebral bodies in 3D CT and MR spine images. Phys Med Biol 56(23):7505–7522

    Article  PubMed  Google Scholar 

  31. Panjabi MM, Takata K, Goel V, Federico D, Oxland T, Duranceau J, Krag M (1991) Thoracic human vertebrae. Quantitative three-dimensional anatomy. Spine 16(8):888–901

    Article  PubMed  CAS  Google Scholar 

  32. Panjabi MM, Goel V, Oxland T, Takata K, Duranceau J, Krag M, Price M (1992) Human lumbar vertebrae. Quantitative three-dimensional anatomy. Spine 17(3):299–306

    Article  PubMed  CAS  Google Scholar 

  33. Masharawi Y, Salame K, Mirovsky Y, Peleg S, Dar G, Steinberg N, Hershkovitz I (2008) Vertebral body shape variation in the thoracic and lumbar spine: characterization of its asymmetry and wedging. Clin Anat 21(1):46–54

    Article  PubMed  Google Scholar 

  34. Štern D, Likar B, Pernuš F, Vrtovec T (2010) Automated detection of spinal centrelines, vertebral bodies and intervertebral discs in CT and MR images of lumbar spine. Phys Med Biol 55(1):247–264

    Article  PubMed  Google Scholar 

  35. Guglielmi G, Stoppino LP, Placentino MG, D’Errico F, Palmieri F (2009) Reproducibility of a semi-automatic method for 6-point vertebral morphometry in a multi-centre trial. Eur J Radiol 69(1):173–178

    Article  PubMed  Google Scholar 

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Acknowledgments

This work has been supported by the Ministry of Education, Science, Culture and Sport, Slovenia, under grants P2-0232, J7-2246, J2-0716, and L2-2023. The authors would like to thank the Clinical Center of Vojvodina, Serbia, for providing the images used in the experiments.

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Authors and Affiliations

  1. Faculty of Electrical Engineering, University of Ljubljana, Tržaška 25, 1000, Ljubljana, Slovenia

    D. Štern, B. Likar, F. Pernuš & T. Vrtovec

  2. Faculty of Medicine, Department of Radiology, Clinical Center of Vojvodina, University of Novi Sad, Hajduk Veljkova 3, 21000, Novi Sad, Serbia

    V. Njagulj

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  1. D. Štern
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  2. V. Njagulj
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Correspondence to D. Štern.

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Štern, D., Njagulj, V., Likar, B. et al. Quantitative vertebral morphometry based on parametric modeling of vertebral bodies in 3D. Osteoporos Int 24, 1357–1368 (2013). https://doi.org/10.1007/s00198-012-2089-4

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  • DOI: https://doi.org/10.1007/s00198-012-2089-4

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