Elsevier

Academic Radiology

Volume 20, Issue 12, December 2013, Pages 1584-1591
Academic Radiology

Original Investigation
Assessment of Trabecular Bone Yield and Post-yield Behavior from High-Resolution MRI-Based Nonlinear Finite Element Analysis at the Distal Radius of Premenopausal and Postmenopausal Women Susceptible to Osteoporosis

https://doi.org/10.1016/j.acra.2013.09.005Get rights and content

Rationale and Objectives

To assess the performance of a nonlinear microfinite element model on predicting trabecular bone yield and post-yield behavior based on high-resolution in vivo magnetic resonance images via the serial reproducibility.

Materials and Methods

The nonlinear model captures material nonlinearity by iteratively adjusting tissue-level modulus based on tissue-level effective strain. It enables simulations of trabecular bone yield and post-yield behavior from micro magnetic resonance images at in vivo resolution by solving a series of nonlinear systems via an iterative algorithm on a desktop computer. Measures of mechanical competence (yield strain/strength, ultimate strain/strength, modulus of resilience, and toughness) were estimated at the distal radius of premenopausal and postmenopausal women (N = 20, age range 50–75) in whom osteoporotic fractures typically occur. Each subject underwent three scans (20.2 ± 14.5 days). Serial reproducibility was evaluated via coefficient of variation (CV) and intraclass correlation coefficient (ICC).

Results

Nonlinear simulations were completed in an average of 14 minutes per three-dimensional image data set involving analysis of 61 strain levels. The predicted yield strain/strength, ultimate strain/strength, modulus of resilience, and toughness had a mean value of 0.78%, 3.09 MPa, 1.35%, 3.48 MPa, 14.30 kPa, and 32.66 kPa, respectively, covering a substantial range by a factor of up to 4. Intraclass correlation coefficient ranged from 0.986 to 0.994 (average 0.991); CV ranged from 1.01% to 5.62% (average 3.6%), with yield strain and toughness having the lowest and highest CV values, respectively.

Conclusions

The data suggest that the yield and post-yield parameters have adequate reproducibility to evaluate treatment effects in interventional studies within short follow-up periods.

Section snippets

Image Acquisition

In vivo micro–magnetic resonance (μMR) images of the right distal radius from 20 female subjects (age range 50–75 years, 17 postmenopausal and 3 premenopausal) were drawn from study described previously (29). All subjects signed an informed consent in accordance with study guidelines of the institutional review board. None of the subjects had a history of fracture, treatment of osteoporosis, or bone cancer. Each subject had been scanned three times (baseline, follow-ups 1 and 2) over the course

Results

The μFE models derived from in vivo μMR images of the distal radius contained an average of 65,200 elements requiring approximately 13.7 minutes per 3D image data set. On average, 62% of the originally acquired volume was retained as the common volume for μFE analysis after retrospective registration. Good visual reproducibility and anatomical alignment are illustrated by the cross-sectional images as well as their BVF maps and 3D volume-rendered images from a subject at three scan time points (

Discussion

Reproducibility of MR or HR-pQCT image-derived structural and elastic parameters has been reported previously 26, 27, 28, 29, 39, 40. However, no prior studies have evaluated the reproducibility of μMR image-based nonlinear μFEA-derived mechanical parameters. The present work demonstrates a high level of longitudinal reproducibility and reliability for both TB yield and post-yield parameters in subjects who, based on their age, are more prone to osteoporosis-associated fracture.

Reproducibility

Acknowledgments

The authors declare that they have no conflicts of interest. This work was supported by National Institutes of Health grants R01 AR055647, R01 AR053156, R01 AR054439, K25 EB007646, and K25 AR060283.

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    Grants supporting the research: National Institutes of Health grants R01 AR055647, R01 AR053156, R01 AR054439, K25 EB007646, and K25 AR060283.

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