Skip to main content
SpringerLink
Log in

Statistical Parametric Mapping of HR-pQCT Images: A Tool for Population-Based Local Comparisons of Micro-Scale Bone Features

  • Published:
Annals of Biomedical Engineering Aims and scope Submit manuscript

Abstract

HR-pQCT enables in vivo multi-parametric assessments of bone microstructure in the distal radius and distal tibia. Conventional HR-pQCT image analysis approaches summarize bone parameters into global scalars, discarding relevant spatial information. In this work, we demonstrate the feasibility and reliability of statistical parametric mapping (SPM) techniques for HR-pQCT studies, which enable population-based local comparisons of bone properties. We present voxel-based morphometry (VBM) to assess trabecular and cortical bone voxel-based features, and a surface-based framework to assess cortical bone features both in cross-sectional and longitudinal studies. In addition, we present tensor-based morphometry (TBM) to assess trabecular and cortical bone structural changes. The SPM techniques were evaluated based on scan-rescan HR-pQCT acquisitions with repositioning of the distal radius and distal tibia of 30 subjects. For VBM and surface-based SPM purposes, all scans were spatially normalized to common radial and tibial templates, while for TBM purposes, rescans (follow-up) were spatially normalized to their corresponding scans (baseline). VBM was evaluated based on maps of local bone volume fraction (BV/TV), homogenized volumetric bone mineral density (vBMD), and homogenized strain energy density (SED) derived from micro-finite element analysis; while the cortical bone framework was evaluated based on surface maps of cortical bone thickness, vBMD, and SED. Voxel-wise and vertex-wise comparisons of bone features were done between the groups of baseline and follow-up scans. TBM was evaluated based on mean square errors of determinants of Jacobians at baseline bone voxels. In both anatomical sites, voxel- and vertex-wise uni- and multi-parametric comparisons yielded non-significant differences, and TBM showed no artefactual bone loss or apposition. The presented SPM techniques demonstrated robust specificity thus warranting their application in future clinical HR-pQCT studies.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8

Similar content being viewed by others

Abbreviations

HR-pQCT:

High-resolution peripheral quantitative computed tomography

SPM:

Statistical parametric mapping

VBM:

Voxel-based morphometry

TBM:

Tensor-based morphometry

BV/TV:

Bone volume fraction

vBMD:

Volumetric bone mineral density

SED:

Strain energy density

µFEA:

Micro-finite element analysis

SIT:

Streamline integral thickness

DetJ:

Determinant of Jacobian

MDT:

Minimum deformation template

References

  1. Ashburner, J., and K. J. Friston. Voxel-based morphometry—the methods. Neuroimage 11(6 Pt 1):805–821, 2000.

    Article  CAS  PubMed  Google Scholar 

  2. Burghardt, A. J., H. R. Buie, A. Laib, S. Majumdar, and S. K. Boyd. Reproducibility of direct quantitative measures of cortical bone microarchitecture of the distal radius and tibia by HR-pQCT. Bone 47(3):519–528, 2010.

    Article  PubMed  PubMed Central  Google Scholar 

  3. Burghardt, A. J., G. J. Kazakia, S. Ramachandran, T. M. Link, and S. Majumdar. Age- and gender-related differences in the geometric properties and biomechanical significance of intracortical porosity in the distal radius and tibia. J. Bone Miner. Res. 25(5):983–993, 2010.

    PubMed  Google Scholar 

  4. Cachier, P., and X. Pennec. 3D non-rigid registration by gradient descent on a Gaussian-windowed similarity measure using convolutions. IEEE Workshop on Mathematical Methods in Biomedical Image Analysis, Proceedings, pp. 182–189, 2000.

  5. Caldairou, B., F. Rousseau, N. Passat, P. Habas, C. Studholme, and C. Heinrich. A non-local fuzzy segmentation method: application to brain MRI. Comput. Anal. Images Patterns Proc. 5702:606–613, 2009.

    Article  Google Scholar 

  6. Carballido-Gamio, J., and S. Majumdar. Atlas-based knee cartilage assessment. Magn. Reson. Med. 66(2):574–583, 2011.

    Article  PubMed  PubMed Central  Google Scholar 

  7. Carballido-Gamio, J., J. S. Bauer, R. Stahl, K. Y. Lee, S. Krause, T. M. Link, and S. Majumdar. Inter-subject comparison of MRI knee cartilage thickness. Med. Image Anal. 12(2):120–135, 2008.

    Article  PubMed  Google Scholar 

  8. Carballido-Gamio, J., T. M. Link, and S. Majumdar. New techniques for cartilage magnetic resonance imaging relaxation time analysis: texture analysis of flattened cartilage and localized intra- and inter-subject comparisons. Magn. Reson. Med. 59(6):1472–1477, 2008.

    Article  PubMed  PubMed Central  Google Scholar 

  9. Carballido-Gamio, J., R. Harnish, I. Saeed, T. Streeper, S. Sigurdsson, S. Amin, E. J. Atkinson, T. M. Therneau, K. Siggeirsdottir, X. Cheng, L. J. Melton, 3rd, J. Keyak, V. Gudnason, S. Khosla, T. B. Harris, and T. F. Lang. Proximal femoral density distribution and structure in relation to age and hip fracture risk in women. J. Bone Miner. Res. 28(3):537–546, 2013.

    Article  PubMed  PubMed Central  Google Scholar 

  10. Carballido-Gamio, J., R. Harnish, I. Saeed, T. Streeper, S. Sigurdsson, S. Amin, E. J. Atkinson, T. M. Therneau, K. Siggeirsdottir, X. Cheng, L. J. Melton, 3rd, J. H. Keyak, V. Gudnason, S. Khosla, T. B. Harris, and T. F. Lang. Structural patterns of the proximal femur in relation to age and hip fracture risk in women. Bone 57(1):290–299, 2013.

    Article  PubMed  Google Scholar 

  11. Carballido-Gamio, J., S. Bonaretti, I. Saeed, R. Harnish, R. Recker, A. J. Burghardt, J. H. Keyak, T. Harris, S. Khosla, and T. Lang. Automatic multi-parametric quantification of the proximal femur with quantitative computed tomography. Quant. Imaging Med. Surg. 5(4):552–568, 2015.

    PubMed  PubMed Central  Google Scholar 

  12. Cheung, A. M., J. D. Adachi, D. A. Hanley, D. L. Kendler, K. S. Davison, R. Josse, J. P. Brown, L. G. Ste-Marie, R. Kremer, M. C. Erlandson, L. Dian, A. J. Burghardt, and S. K. Boyd. 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. 11(2):136–146, 2013.

    Article  PubMed  PubMed Central  Google Scholar 

  13. Davatzikos, C., M. Vaillant, S. M. Resnick, J. L. Prince, S. Letovsky, and R. N. Bryan. A computerized approach for morphological analysis of the corpus callosum. J. Comput. Assist. Tomogr. 20(1):88–97, 1996.

    Article  CAS  PubMed  Google Scholar 

  14. Friston, K. J., C. D. Frith, P. F. Liddle, R. J. Dolan, A. A. Lammertsma, and R. S. Frackowiak. The relationship between global and local changes in PET scans. J. Cereb. Blood Flow Metab. 10(4):458–466, 1990.

    Article  CAS  PubMed  Google Scholar 

  15. Genovese, C. R., N. A. Lazar, and T. Nichols. Thresholding of statistical maps in functional neuroimaging using the false discovery rate. Neuroimage 15(4):870–878, 2002.

    Article  PubMed  Google Scholar 

  16. Griffith, J. F., K. Engelke, and H. K. Genant. Looking beyond bone mineral density: imaging assessment of bone quality. Ann. N. Y. Acad. Sci. 1192:45–56, 2010.

    Article  PubMed  Google Scholar 

  17. Hazrati Marangalou, J., F. Eckstein, V. Kuhn, K. Ito, M. Cataldi, F. Taddei, and B. van Rietbergen. Locally measured microstructural parameters are better associated with vertebral strength than whole bone density. Osteoporos. Int. 25(4):1285–1296, 2014.

    Article  CAS  PubMed  Google Scholar 

  18. Hazrati Marangalou, J., K. Ito, F. Taddei, and B. van Rietbergen. Inter-individual variability of bone density and morphology distribution in the proximal femur and T12 vertebra. Bone 60:213–220, 2014.

    Article  PubMed  Google Scholar 

  19. Hua, X., A. D. Leow, J. G. Levitt, R. Caplan, P. M. Thompson, and A. W. Toga. Detecting brain growth patterns in normal children using tensor-based morphometry. Hum. Brain Mapp. 30(1):209–219, 2009.

    Article  PubMed  PubMed Central  Google Scholar 

  20. Jones, S. E., B. R. Buchbinder, and I. Aharon. Three-dimensional mapping of cortical thickness using Laplace’s equation. Hum. Brain Mapp. 11(1):12–32, 2000.

    Article  CAS  PubMed  Google Scholar 

  21. Kazakia, G. J., J. A. Nirody, G. Bernstein, M. Sode, A. J. Burghardt, and S. Majumdar. Age- and gender-related differences in cortical geometry and microstructure: Improved sensitivity by regional analysis. Bone 52(2):623–631, 2013.

    Article  PubMed  Google Scholar 

  22. Krebs, A., C. Graeff, I. Frieling, B. Kurz, W. Timm, K. Engelke, and C. C. Gluer. High resolution computed tomography of the vertebrae yields accurate information on trabecular distances if processed by 3D fuzzy segmentation approaches. Bone 44(1):145–152, 2009.

    Article  PubMed  Google Scholar 

  23. Laib, A., H. J. Hauselmann, and P. Ruegsegger. In vivo high resolution 3D-QCT of the human forearm. Technol. Health Care 6(5–6):329–337, 1998.

    CAS  PubMed  Google Scholar 

  24. Lang, T. F., I. H. Saeed, T. Streeper, J. Carballido-Gamio, R. J. Harnish, L. A. Frassetto, S. M. Lee, J. D. Sibonga, J. H. Keyak, B. A. Spiering, C. M. Grodsinsky, J. J. Bloomberg, and P. R. Cavanagh. Spatial heterogeneity in the response of the proximal femur to two lower-body resistance exercise regimens. J. Bone Miner. Res. 29(6):1337–1345, 2014.

    Article  PubMed  PubMed Central  Google Scholar 

  25. Li, W., I. Kezele, D. L. Collins, A. Zijdenbos, J. Keyak, J. Kornak, A. Koyama, I. Saeed, A. Leblanc, T. Harris, Y. Lu, and T. Lang. Voxel-based modeling and quantification of the proximal femur using inter-subject registration of quantitative CT images. Bone 41(5):888–895, 2007.

    Article  PubMed  PubMed Central  Google Scholar 

  26. Li, W., J. Kornak, T. Harris, J. Keyak, C. Li, Y. Lu, X. Cheng, and T. Lang. Identify fracture-critical regions inside the proximal femur using statistical parametric mapping. Bone 44(4):596–602, 2009.

    Article  PubMed  Google Scholar 

  27. Manske, S. L., Y. Zhu, C. Sandino, and S. K. Boyd. Human trabecular bone microarchitecture can be assessed independently of density with second generation HR-pQCT. Bone 79:213–221, 2015.

    Article  CAS  PubMed  Google Scholar 

  28. Nirody, J. A., K. P. Cheng, R. M. Parrish, A. J. Burghardt, S. Majumdar, T. M. Link, and G. J. Kazakia. Spatial distribution of intracortical porosity varies across age and sex. Bone 75:88–95, 2015.

    Article  PubMed  PubMed Central  Google Scholar 

  29. Nishiyama, K. K., and E. Shane. Clinical imaging of bone microarchitecture with HR-pQCT. Curr. Osteoporos. Rep. 11(2):147–155, 2013.

    Article  PubMed  PubMed Central  Google Scholar 

  30. Pialat, J. B., A. J. Burghardt, M. Sode, T. M. Link, and S. Majumdar. Visual grading of motion induced image degradation in high resolution peripheral computed tomography: impact of image quality on measures of bone density and micro-architecture. Bone 50(1):111–118, 2012.

    Article  CAS  PubMed  Google Scholar 

  31. Poole, K. E., G. M. Treece, G. R. Ridgway, P. M. Mayhew, J. Borggrefe, and A. H. Gee. Targeted regeneration of bone in the osteoporotic human femur. PLoS ONE 6(1):e16190, 2011.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Poole, K. E., G. M. Treece, P. M. Mayhew, J. Vaculik, P. Dungl, M. Horak, J. J. Stepan, and A. H. Gee. Cortical thickness mapping to identify focal osteoporosis in patients with hip fracture. PLoS ONE 7(6):e38466, 2012.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Poole, K. E., G. M. Treece, A. H. Gee, J. P. Brown, M. R. McClung, A. Wang, and C. Libanati. Denosumab rapidly increases cortical bone in key locations of the femur: a 3D bone mapping study in women with osteoporosis. J. Bone Miner. Res. 30:46–54, 2014.

    Article  Google Scholar 

  34. Rajagopalan, V., J. Scott, P. A. Habas, K. Kim, F. Rousseau, O. A. Glenn, A. J. Barkovich, and C. Studholme. Mapping directionality specific volume changes using tensor based morphometry: an application to the study of gyrogenesis and lateralization of the human fetal brain. Neuroimage 63(2):947–958, 2012.

    Article  PubMed  PubMed Central  Google Scholar 

  35. Robbins, S., A. C. Evans, D. L. Collins, and S. Whitesides. Tuning and comparing spatial normalization methods. Med. Image Anal. 8(3):311–323, 2004.

    Article  PubMed  Google Scholar 

  36. Saha, P. K., Y. Liu, C. Chen, D. Jin, E. M. Letuchy, Z. Xu, R. E. Amelon, T. L. Burns, J. C. Torner, S. M. Levy, and C. A. Calarge. Characterization of trabecular bone plate-rod microarchitecture using multirow detector CT and the tensor scale: Algorithms, validation, and applications to pilot human studies. Med. Phys. 42(9):5410–5425, 2015.

    Article  PubMed  PubMed Central  Google Scholar 

  37. Schwartzman, A., R. F. Dougherty, and J. E. Taylor. Cross-subject comparison of principal diffusion direction maps. Magn. Reson. Med. 53(6):1423–1431, 2005.

    Article  PubMed  Google Scholar 

  38. Sode, M., A. J. Burghardt, G. J. Kazakia, T. M. Link, and S. Majumdar. Regional variations of gender-specific and age-related differences in trabecular bone structure of the distal radius and tibia. Bone 46(6):1652–1660, 2010.

    Article  PubMed  PubMed Central  Google Scholar 

  39. Thompson, D. W. On Growth and Form (New ed.). Cambridge: Cambridge University Press, 1942.

    Google Scholar 

  40. Thompson, P. M., and L. G. Apostolova. Computational anatomical methods as applied to ageing and dementia. Br. J. Radiol. 80(Spec. No. 2):S78–S91, 2007.

    Article  PubMed  Google Scholar 

  41. Treece, G. M., and A. H. Gee. Independent measurement of femoral cortical thickness and cortical bone density using clinical CT. Med. Image Anal. 20(1):249–264, 2015.

    Article  CAS  PubMed  Google Scholar 

  42. Treece, G. M., A. H. Gee, P. M. Mayhew, and K. E. Poole. High resolution cortical bone thickness measurement from clinical CT data. Med. Image Anal. 14(3):276–290, 2010.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Treece, G. M., K. E. Poole, and A. H. Gee. Imaging the femoral cortex: thickness, density and mass from clinical CT. Med. Image Anal. 16(5):952–965, 2012.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Treece, G. M., A. H. Gee, C. Tonkin, S. K. Ewing, P. M. Cawthon, D. M. Black, K. E. Poole, and for the Osteoporotic Fractures in Men (MrOS) Study. Predicting hip fracture type with cortical bone mapping (CBM) in the osteoporotic fractures in men (MrOS) study. J. Bone Miner. Res. 30(11):2067–2077, 2015.

    Article  PubMed  PubMed Central  Google Scholar 

  45. Vasilic, B., C. S. Rajapakse, and F. W. Wehrli. Classification of trabeculae into three-dimensional rodlike and platelike structures via local inertial anisotropy. Med. Phys. 36(7):3280–3291, 2009.

    Article  PubMed  PubMed Central  Google Scholar 

  46. Vercauteren, T., X. Pennec, A. Perchant, and N. Ayache. Non-parametric diffeomorphic image registration with the demons algorithm. Med. Image Comput. Assist. Interv. 10(Pt 2):319–326, 2007.

    Google Scholar 

  47. Whitmarsh, T., G. M. Treece, A. H. Gee, and K. E. Poole. mapping bone changes at the proximal femoral cortex of postmenopausal women in response to alendronate and teriparatide alone, combined or sequentially. J. Bone Miner. Res. 30(7):1309–1318, 2015.

    Article  CAS  PubMed  Google Scholar 

  48. Zebaze, R., A. Ghasem-Zadeh, A. Mbala, and E. Seeman. A new method of segmentation of compact-appearing, transitional and trabecular compartments and quantification of cortical porosity from high resolution peripheral quantitative computed tomographic images. Bone 54(1):8–20, 2013.

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgments

This work was supported by the NIH/NIAMS under Grants R01AR068456, R01AR060700, R01AR064140 and P30AR066262.

Author information

Authors and Affiliations

  1. Department of Radiology, University of Colorado Denver, Denver, CO, USA

    Julio Carballido-Gamio

  2. Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA

    Serena Bonaretti

  3. Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, USA

    Galateia J. Kazakia, Sharmila Majumdar, Thomas F. Lang & Andrew J. Burghardt

  4. Division of Endocrinology, Diabetes, Metabolism and Nutrition, Department of Internal Medicine, College of Medicine, Mayo Clinic, Rochester, MN, USA

    Sundeep Khosla

Authors
  1. Julio Carballido-Gamio
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Serena Bonaretti
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Galateia J. Kazakia
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Sundeep Khosla
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Sharmila Majumdar
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Thomas F. Lang
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Andrew J. Burghardt
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Julio Carballido-Gamio.

Ethics declarations

Conflicts of Interest

The authors have no relevant conflicts of interest to disclose.

Additional information

Associate Editor Mona Kamal Marei oversaw the review of this article.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Carballido-Gamio, J., Bonaretti, S., Kazakia, G.J. et al. Statistical Parametric Mapping of HR-pQCT Images: A Tool for Population-Based Local Comparisons of Micro-Scale Bone Features. Ann Biomed Eng 45, 949–962 (2017). https://doi.org/10.1007/s10439-016-1754-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10439-016-1754-8

Keywords

Search

Navigation