Assessing Methods for Characterising Local and Global Structural and Biomechanical Properties of the Trabecular Bone Network

Scaling relations between trabecular bone volume fraction and microstructure at different skeletal sites

In this study, we investigated the scaling relations between trabecular bone volume fraction (BV/TV) and parameters of the trabecular microstructure at different skeletal sites. Cylindrical bone samples with a diameter of 8mm were harvested from different skeletal sites of 154 human donors in vitro: 87 from the distal radius, 59/69 from the thoracic/lumbar spine, 51 from the femoral neck, and 83 from the greater trochanter. μCT images were obtained with an isotropic spatial resolution of 26μm. BV/TV and trabecular microstructure parameters (TbN, TbTh, TbSp, scaling indices (< > and σ of α and αz), and Minkowski Functionals (Surface, Curvature, Euler)) were computed for each sample. The regression coefficient β was determined for each skeletal site as the slope of a linear fit in the double-logarithmic representations of the correlations of BV/TV versus the respective microstructure parameter. Statistically significant correlation coefficients ranging from r=0.36 to r=0.97 were observed for BV/TV versus microstructure parameters, except for Curvature and Euler. The regression coefficients β were 0.19 to 0.23 (TbN), 0.21 to 0.30 (TbTh), -0.28 to -0.24 (TbSp), 0.58 to 0.71 (Surface) and 0.12 to 0.16 (<α>), 0.07 to 0.11 (<αz>), -0.44 to -0.30 (σ(α)), and -0.39 to -0.14 (σ(αz)) at the different skeletal sites. The 95% confidence intervals of β overlapped for almost all microstructure parameters at the different skeletal sites. The scaling relations were independent of vertebral fracture status and similar for subjects aged 60-69, 70-79, and >79years. In conclusion, the bone volume fraction-microstructure scaling relations showed a rather universal character.

Prediction of bone strength by μCT and MDCT-based finite-element-models: how much spatial resolution is needed?

OBJECTIVES

Finite-element-models (FEM) are a promising technology to predict bone strength and fracture risk. Usually, the highest spatial resolution technically available is used, but this requires excessive computation time and memory in numerical simulations of large volumes. Thus, FEM were compared at decreasing resolutions with respect to local strain distribution and prediction of failure load to (1) validate MDCT-based FEM and to (2) optimize spatial resolution to save computation time.

MATERIALS AND METHODS

20 cylindrical trabecular bone specimens (diameter 12 mm, length 15-20mm) were harvested from elderly formalin-fixed human thoracic spines. All specimens were examined by micro-CT (isotropic resolution 30 μm) and whole-body multi-row-detector computed tomography (MDCT, 250 μm × 250 μm × 500 μm). The resolution of all datasets was lowered in eight steps to ~ 2,000 μm × 2000 μm × 500 μm and FEM were calculated at all resolutions. Failure load was determined by biomechanical testing. Probability density functions of local micro-strains were compared in all datasets and correlations between FEM-based and biomechanically measured failure loads were determined.

RESULTS

The distribution of local micro-strains was similar for micro-CT and MDCT at comparable resolutions and showed a shift toward higher average values with decreasing resolution, corresponding to the increasing apparent trabecular thickness. Small micro-strains (εeff<0.005) could be calculated down to 250 μm × 250 μm × 500 μm. Biomechanically determined failure load showed significant correlations with all FEM, up to r=0.85 and did not significantly change with lower resolution but decreased with high thresholds, due to loss of trabecular connectivity.

CONCLUSION

When choosing connectivity-preserving thresholds, both micro-CT- and MDCT-based finite-element-models well predicted failure load and still accurately revealed the distribution of local micro-strains in spatial resolutions, available in vivo (250 μm × 250 μm × 500 μm), that thus seemed to be the optimal compromise between high accuracy and low computation time.