Using 3D image registration to maximize the reproducibility of longitudinal bone strength assessment by HR-pQCT and finite element analysis

Abstract

Summary

We developed and validated a finite element (FE) approach for longitudinal high-resolution peripheral quantitative computed tomography (HR-pQCT) studies using 3D image registration to account for misalignment between images. This reduced variability in longitudinal FE estimates and improved our ability to measure in vivo changes in HR-pQCT studies of bone strength.

Introduction

We developed and validated a finite element (FE) approach for longitudinal high-resolution peripheral quantitative computed tomography (HR-pQCT) studies using 3D rigid-body registration (3DR) to maximize reproducibility by accounting for misalignment between images.

Methods

In our proposed approach, we used the full common bone volume defined by 3DR to estimate standard FE parameters. Using standard HR-pQCT imaging protocols, we validated the 3DR approach with ex vivo samples of the distal radius (n = 10, four repeat scans) by assessing whether 3DR can reduce measurement variability from repositioning error. We used in vivo data (n = 40, five longitudinal scans) to assess the sensitivity of 3DR to detect changes in bone strength at the distal radius by the standard deviation of the rate of change (σ), where the ideal value of σ is minimized to define true change. FE estimates by 3DR were compared to estimates by no registration (NR) and slice-matching (SM).

Results

Group-wise comparisons of ex vivo variation (CVRMS, %) found that FE measurement precision was improved by SM (CVRMS < 0.80%) and 3DR (CVRMS < 0.62%) compared to NR (CVRMS~2%), and 3DR was advantageous as repositioning error increased. Longitudinal in vivo reproducibility was minimized by 3DR for failure load estimates (σ = 0.008 kN/month).

Conclusion

Although 3D registration cannot negate motion artifacts, it plays an important role in detecting and reducing variability in FE estimates for longitudinal HR-pQCT data and is well suited for estimating effects of interventions in in vivo longitudinal studies of bone strength.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3

Data availability

The data are not publicly available. Upon reasonable requests to the corresponding author, data may be shared.

References

  1. 1.

    Cheung AM, Adachi JD, Hanley DA, Kendler DL, Davison KS, Josse R, Brown JP, Ste-Marie LG, Kremer R, Erlandson MC, Dian L, Burghardt AJ, Boyd SK (2013) 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. https://doi.org/10.1007/s11914-013-0140-9

    Article  PubMed  PubMed Central  Google Scholar 

  2. 2.

    Boutroy S, Bouxsein ML, Munoz F, Delmas PD (2005) In vivo assessment of trabecular bone microarchitecture by high-resolution peripheral quantitative computed tomography. J Clin Endocrinol Metab 90(12):6508–6515. https://doi.org/10.1210/jc.2005-1258

    CAS  Article  PubMed  Google Scholar 

  3. 3.

    Whittier DE, Boyd SK, Burghardt AJ, Paccou J, Ghasem-Zadeh A, Chapurlat R, Engelke K, Bouxsein ML (2020) Guidelines for the assessment of bone density and microarchitecture in vivo using high-resolution peripheral quantitative computed tomography. Osteoporos Int 31(9):1607–1627. https://doi.org/10.1007/s00198-020-05438-5

    CAS  Article  PubMed  Google Scholar 

  4. 4.

    Agarwal S, Rosete F, Zhang C, McMahon DJ, Guo XE, Shane E, Nishiyama KK (2016) In vivo assessment of bone structure and estimated bone strength by first- and second-generation HR-pQCT. Osteoporos Int 27(10):2955–2966. https://doi.org/10.1007/s00198-016-3621-8

    CAS  Article  PubMed  Google Scholar 

  5. 5.

    Manske SL, Zhu Y, Sandino C, Boyd SK (2015) Human trabecular bone microarchitecture can be assessed independently of density with second generation HR-pQCT. Bone 79:213–221. https://doi.org/10.1016/j.bone.2015.06.006

    CAS  Article  PubMed  Google Scholar 

  6. 6.

    Burghardt AJ, Kazakia GJ, Sode M, de Papp AE, Link TM, Majumdar S (2010) A longitudinal HR-pQCT study of alendronate treatment in postmenopausal women with low bone density: relations among density, cortical and trabecular microarchitecture, biomechanics, and bone turnover. J Bone Miner Res 25(12):2282–2295. https://doi.org/10.1002/jbmr.157

    CAS  Article  Google Scholar 

  7. 7.

    Burt LA, Billington EO, Rose MS, Raymond DA, Hanley DA, Boyd SK (2019) Effect of high-dose vitamin D supplementation on volumetric bone density and bone strength: a randomized clinical trial. JAMA 322(8):736–745. https://doi.org/10.1001/jama.2019.11889

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  8. 8.

    Tsai JN, Nishiyama KK, Lin D, Yuan A, Lee H, Bouxsein ML, Leder BZ (2017) Effects of denosumab and teriparatide transitions on bone microarchitecture and estimated strength: the DATA-Switch HR-pQCT study. J Bone Miner Res 32(10):2001–2009. https://doi.org/10.1002/jbmr.3198

    CAS  Article  PubMed  Google Scholar 

  9. 9.

    Gabel L, Macdonald HM, Nettlefold L, McKay HA (2017) Physical activity, sedentary time, and bone strength from childhood to early adulthood: a mixed longitudinal HR-pQCT study. J Bone Miner Res 32(7):1525–1536. https://doi.org/10.1002/jbmr.3115

    CAS  Article  PubMed  Google Scholar 

  10. 10.

    Vico L, van Rietbergen B, Vilayphiou N, Linossier M-T, Locrelle H, Normand M, Zouch M, Gerbaix M, Bonnet N, Novikov V, Thomas T, Vassilieva G (2017) Cortical and trabecular bone microstructure did not recover at weight-bearing skeletal sites and progressively deteriorated at non-weight-bearing sites during the year following international space station missions. J Bone Miner Res 32(10):2010–2021. https://doi.org/10.1002/jbmr.3188

    CAS  Article  PubMed  Google Scholar 

  11. 11.

    Kemp TD, de Bakker CMJ, Gabel L, Hanley DA, Billington EO, Burt LA, Boyd SK (2020) Longitudinal bone microarchitectural changes are best detected using image registration. Osteoporos Int 31(10):1995–2005. https://doi.org/10.1007/s00198-020-05449-2

    CAS  Article  PubMed  Google Scholar 

  12. 12.

    Ellouz R, Chapurlat R, van Rietbergen B, Christen P, Pialat JB, Boutroy S (2014) Challenges in longitudinal measurements with HR-pQCT: evaluation of a 3D registration method to improve bone microarchitecture and strength measurement reproducibility. Bone 63:147–157. https://doi.org/10.1016/j.bone.2014.03.001

    Article  PubMed  Google Scholar 

  13. 13.

    van Rietbergen B, Ito K (2015) A survey of micro-finite element analysis for clinical assessment of bone strength: the first decade. J Biomech 48(5):832–841. https://doi.org/10.1016/j.jbiomech.2014.12.024

    Article  PubMed  Google Scholar 

  14. 14.

    Burt LA, Gaudet S, Kan M, Rose MS, Billington EO, Boyd SK, Hanley DA (2018) Methods and procedures for: a randomized double-blind study investigating dose-dependent longitudinal effects of vitamin D supplementation on bone health. Contemp Clin Trials 67:68–73. https://doi.org/10.1016/j.cct.2018.02.009

    Article  PubMed  Google Scholar 

  15. 15.

    Sode M, Burghardt AJ, Pialat JB, Link TM, Majumdar S (2011) Quantitative characterization of subject motion in HR-pQCT images of the distal radius and tibia. Bone 48(6):1291–1297. https://doi.org/10.1016/j.bone.2011.03.755

    Article  PubMed  PubMed Central  Google Scholar 

  16. 16.

    Buie HR, Campbell GM, Klinck RJ, MacNeil JA, Boyd SK (2007) Automatic segmentation of cortical and trabecular compartments based on a dual threshold technique for in vivo micro-CT bone analysis. Bone 41(4):505–515. https://doi.org/10.1016/j.bone.2007.07.007

    Article  PubMed  Google Scholar 

  17. 17.

    Burghardt AJ, Buie HR, Laib A, Majumdar S, Boyd SK (2010) Reproducibility of direct quantitative measures of cortical bone microarchitecture of the distal radius and tibia by HR-pQCT. Bone 47(3):519–528. https://doi.org/10.1016/j.bone.2010.05.034

    Article  PubMed  PubMed Central  Google Scholar 

  18. 18.

    Whittier DE, Manske SL, Kiel DP, Bouxsein M, Boyd SK (2018) Harmonizing finite element modelling for non-invasive strength estimation by high-resolution peripheral quantitative computed tomography. J Biomech 80:63–71. https://doi.org/10.1016/j.jbiomech.2018.08.030

    Article  PubMed  PubMed Central  Google Scholar 

  19. 19.

    van Rietbergen B, Weinans H, Huiskes R, Odgaard A (1995) A new method to determine trabecular bone elastic properties and loading using micromechanical finite-element models. J Biomech 28(1):69–81. https://doi.org/10.1016/0021-9290(95)80008-5

    Article  PubMed  Google Scholar 

  20. 20.

    Pistoia W, van Rietbergen B, Lochmuller EM, Lill CA, Eckstein F, Ruegsegger P (2002) Estimation of distal radius failure load with micro-finite element analysis models based on three-dimensional peripheral quantitative computed tomography images. Bone 30(6):842–848. https://doi.org/10.1016/s8756-3282(02)00736-6

    CAS  Article  PubMed  Google Scholar 

  21. 21.

    Glüer CC, Blake G, Lu Y, Blunt BA, Jergas M, Genant HK (1995) Accurate assessment of precision errors: how to measure the reproducibility of bone densitometry techniques. Osteoporos Int 5(4):262–270. https://doi.org/10.1007/bf01774016

    Article  PubMed  Google Scholar 

  22. 22.

    Glüer CC (1999) Monitoring skeletal changes by radiological techniques. J Bone Miner Res 14(11):1952–1962. https://doi.org/10.1359/jbmr.1999.14.11.1952

    Article  PubMed  Google Scholar 

  23. 23.

    Shepherd JA, Lu Y (2007) A generalized least significant change for individuals measured on different DXA systems. J Clin Densitom 10(3):249–258. https://doi.org/10.1016/j.jocd.2007.05.002

    Article  PubMed  Google Scholar 

  24. 24.

    MacNeil JA, Boyd SK (2008) Improved reproducibility of high-resolution peripheral quantitative computed tomography for measurement of bone quality. Med Eng Phys 30(6):792–799. https://doi.org/10.1016/j.medengphy.2007.11.003

    Article  PubMed  Google Scholar 

  25. 25.

    Paggiosi MA, Eastell R, Walsh JS (2014) Precision of high-resolution peripheral quantitative computed tomography measurement variables: influence of gender, examination site, and age. Calcif Tissue Int 94(2):191–201. https://doi.org/10.1007/s00223-013-9798-3

    CAS  Article  PubMed  Google Scholar 

  26. 26.

    Pauchard Y, Ayres FJ, Boyd SK (2011) Automated quantification of three-dimensional subject motion to monitor image quality in high-resolution peripheral quantitative computed tomography. Phys Med Biol 56(20):6523–6543. https://doi.org/10.1088/0031-9155/56/20/001

    Article  PubMed  Google Scholar 

  27. 27.

    Bonaretti S, Vilayphiou N, Chan CM, Yu A, Nishiyama K, Liu D, Boutroy S, Ghasem-Zadeh A, Boyd SK, Chapurlat R, McKay H, Shane E, Bouxsein ML, Black DM, Majumdar S, Orwoll ES, Lang TF, Khosla S, Burghardt AJ (2017) Operator variability in scan positioning is a major component of HR-pQCT precision error and is reduced by standardized training. Osteoporos Int 28(1):245–257. https://doi.org/10.1007/s00198-016-3705-5

    CAS  Article  PubMed  Google Scholar 

  28. 28.

    Boyd SK (2008) Site-specific variation of bone micro-architecture in the distal radius and tibia. J Clin Densitom 11(3):424–430. https://doi.org/10.1016/j.jocd.2007.12.013

    Article  PubMed  Google Scholar 

Download references

Acknowledgements

The authors thank J.M. Allan, J.A. Allan, S. Gaudet, M. Kan, and B. Love for participant recruitment and A. Cooke, S. Kwong, and D. Raymond for scan acquisition and analysis. The authors acknowledge the Natural Sciences and Engineering Research Council (NSERC) of Canada (Discovery Grant: RGPIN-2019-04135) and Pure North S’Energy Foundation by an investigator-initiated grant for funding this study. The authors also thank the study participants for generously donating their time to support our research.

Funding

This study was funded by the Natural Sciences and Engineering Research Council (NSERC) of Canada (Discovery Grant: RGPIN-2019-04135) and Pure North S’Energy Foundation in response to an investigator-initiated research proposal.

Author information

Affiliations

Authors

Contributions

Study design: RMP, TDK, LAB, SKB. Data acquisition & analysis: RMP, TDK, LAB, SKB. Drafting of manuscript: RMP, TDK, LAB, EOB, DAH, SKB. All authors contributed to revising the manuscript and approved the final version of the submitted manuscript. RMP and SKB take responsibility for the integrity of the data analysis, and all authors agree to be accountable for the work.

Corresponding author

Correspondence to S. K. Boyd.

Ethics declarations

Ethics approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Conflict of interest

RMP, TDK, LAB and DAH have nothing to declare. EOB has previously received honoraria from Amgen and Eli Lilly, and a research grant from Amgen. SKB has received honorariums from Amgen and Servier.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

ESM 1

(JPG 90 kb)

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Plett, R.M., Kemp, T.D., Burt, L.A. et al. Using 3D image registration to maximize the reproducibility of longitudinal bone strength assessment by HR-pQCT and finite element analysis. Osteoporos Int (2021). https://doi.org/10.1007/s00198-021-05896-5

Download citation

Keywords

  • 3D registration
  • Bone strength
  • Finite element analysis
  • HR-pQCT
  • Longitudinal analysis