Finite Element Analysis-Based Vertebral Bone Strength Prediction Using MDCT Data: How Low Can We Go?

Objective:To study the impact of dose reduction in MDCT images through tube current reduction or sparse sampling on the vertebral bone strength prediction using finite element (FE) analysis for fracture risk assessment. Methods:Routine MDCT data covering lumbar vertebrae of 12 subjects (six male; six female; 74.70 +/- 9.13 years old) were included in this study. Sparsely sampled and virtually reduced tube current-based MDCT images were computed using statistical iterative reconstruction (SIR) with reduced dose levels at 50, 25, and 10% of the tube current and original projections, respectively. Subject-specific static non-linear FE analyses were performed on vertebra models (L1, L2, and L3) 3-D-reconstructed from those dose-reduced MDCT images to predict bone strength. Coefficient of correlation (R-2), Bland-Altman plots, and root mean square coefficient of variation (RMSCV) were calculated to find the variation in the FE-predicted strength at different dose levels, using high-intensity dose-based strength as the reference. Results:FE-predicted failure loads were not significantly affected by up to 90% dose reduction through sparse sampling (R-2= 0.93, RMSCV = 8.6% for 50%;R-2= 0.89, RMSCV = 11.90% for 75%;R-2= 0.86, RMSCV = 11.30% for 90%) and up to 50% dose reduction through tube current reduction method (R-2= 0.96, RMSCV = 12.06%). However, further reduction in dose with the tube current reduction method affected the ability to predict the failure load accurately (R-2= 0.88, RMSCV = 22.04% for 75%;R-2= 0.43, RMSCV = 54.18% for 90%). Conclusion:Results from this study suggest that a 50% radiation dose reduction through reduced tube current and a 90% radiation dose reduction through sparse sampling can be used to predict vertebral bone strength. Our findings suggest that the sparse sampling-based method performs better than the tube current-reduction method in generating images required for FE-based bone strength prediction models.