Osteoporosis is characterized by bony material loss and decreased bone strength leading to a significant increase in fracture risk. Patient-specific quantitative computed tomography (QCT) finite element (FE) models may be used to predict fracture under physiological loading. Material properties for the FE models used to predict fracture are obtained by converting grayscale values from the CT into volumetric bone mineral density (vBMD) using calibration phantoms. If there are any variations arising from the CT acquisition protocol, vBMD estimation and material property assignment could be affected, thus, affecting fracture risk prediction. We hypothesized that material property assignments may be dependent on scanning and postprocessing settings including voltage, current, and reconstruction kernel, thus potentially having an effect in fracture risk prediction. A rabbit femur and a standard calibration phantom were imaged by QCT using different protocols. Cortical and cancellous regions were segmented, their average Hounsfield unit (HU) values obtained and converted to vBMD. Estimated vBMD for the cortical and cancellous regions were affected by voltage and kernel but not by current. Our study demonstrated that there exists a significant variation in the estimated vBMD values obtained with different scanning acquisitions. In addition, the large noise differences observed utilizing different scanning parameters could have an important negative effect on small subregions containing fewer voxels.

Quantitative computed tomography based finite element analysis of the femur is currently being investigated as a method for non-invasive stiffness and strength predictions of the proximal femur. The specific objective of this study was to determine better conversion relationships from QCT-derived bone density to elastic modulus, in order to achieve accurate predictions of the overall femoral stiffness in a fall-on-the-hip loading configuration. Twenty-two femurs were scanned, segmented and meshed for finite element analysis. The elastic moduli of the elements were assigned according to the average density in the element. The femurs were then tested to fracture and force-displacement data were collected to calculate femoral stiffness. Using a training set of nine femurs, finite element analyses were performed and the parameters of the density-elastic modulus relationship were iteratively adjusted to obtain optimal stiffness predictions in a least-squares sense. The results were then validated on the remaining 13 femurs. Our novel procedure resulted in parameter identification of both power and sigmoid functions for density-elastic modulus conversion for this specific loading scenario. Our in situ estimated power law achieved improved predictions compared to published power laws, and the sigmoid function yielded even smaller prediction errors. In the future, these results will be used to further improve the femoral strength predictions of our finite element models.

Quantitative computed tomography-based finite-element analysis (QCT/FEA) has become increasingly popular in an attempt to understand and possibly reduce vertebral fracture risk. It is known that scanning acquisition settings affect Hounsfield units (HU) of the CT voxels. Material properties assignments in QCT/FEA, relating HU to Young's modulus, are performed by applying empirical equations. The purpose of this study was to evaluate the effect of QCT scanning protocols on predicted stiffness values from finite-element models. One fresh frozen cadaveric torso and a QCT calibration phantom were scanned six times varying voltage and current and reconstructed to obtain a total of 12 sets of images. Five vertebrae from the torso were experimentally tested to obtain stiffness values. QCT/FEA models of the five vertebrae were developed for the 12 image data resulting in a total of 60 models. Predicted stiffness was compared to the experimental values. The highest percent difference in stiffness was approximately 480% (80 kVp, 110 mAs, U70), while the lowest outcome was ∼1% (80 kVp, 110 mAs, U30). There was a clear distinction between reconstruction kernels in predicted outcomes, whereas voltage did not present a clear influence on results. The potential of QCT/FEA as an improvement to conventional fracture risk prediction tools is well established. However, it is important to establish research protocols that can lead to results that can be translated to the clinical setting.