Effect of Statistically Iterative Image Reconstruction on Vertebral Bone Strength Prediction Using Bone Mineral Density and Finite Element Modeling: A Preliminary Study

Statistical iterative reconstruction (SIR) using multidetector computed tomography (MDCT) is a promising alternative to standard filtered back projection (FBP), because of lower noise generation while maintaining image quality. Hence, we investigated the feasibility of SIR in predicting MDCT-based bone mineral density (BMD) and vertebral bone strength from finite element (FE) analysis. The BMD and FE-predicted bone strength derived from MDCT images reconstructed using standard FBP (F FBP) and SIR with (F SIR) and without regularization (F SIRB0) were validated against experimental failure loads (F exp). Statistical iterative reconstruction produced the best quality images with regard to noise, signal-to-noise ratio, and contrast-to-noise ratio. F exp significantly correlated with F FBP, F SIR, and F SIRB0. F FBP had a significant correlation with F SIRB0 and F SIR. The BMD derived from FBP, SIRB0, and SIR were significantly correlated. Effects of regularization should be further investigated with FE and BMD analysis to allow for an optimal iterative reconstruction algorithm to be implemented in an in vivo scenario.