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.
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Data availability
The data are not publicly available. Upon reasonable requests to the corresponding author, data may be shared.
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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.
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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.
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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.
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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.
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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 32, 1849–1857 (2021). https://doi.org/10.1007/s00198-021-05896-5
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DOI: https://doi.org/10.1007/s00198-021-05896-5