European Society of Biomechanics S.M. Perren Award 2008: using temporal trends of 3D bone micro-architecture to predict bone quality.

In longitudinal studies, three-dimensional (3D) bone images are acquired at sequential time points essentially resulting in four-dimensional (4D) data for an individual. Based on the 4D data, we propose to calculate temporal trends and project these trends to estimate future bone architecture. Multiple consecutive deformation fields, calculated with Demons deformable image registration algorithm, were extrapolated on a voxel-by-voxel basis. Test data were from in vivo micro-computed tomography (microCT) scans of the proximal tibia of Wistar rats that were either ovariectomized (OVX; N=5) or sham operated (SHAM; N=6). Measurements performed at baseline, 4 and 8 weeks were the basis to predict the 12 week data. Predicted and actual 12 week data were compared using qualitative (3D rendering) and quantitative (geometry, morphology and micro-finite element, microFE) methods. The results indicated a voxel-based linear extrapolation scheme yielded mean geometric errors that were smaller than the voxel size of 15 microm. Key morphological parameters that were estimated included bone volume ratio (BV/TV; mean error 0.4%, maximum error 9%), trabecular thickness (Tb.Th; -1.1%, 11%), connectivity density (Conn.D; 9.0%, 18.5%) and the apparent Young's modulus (E(1); 6.0%, 32%). These data demonstrated a promising and novel approach for quantitatively capturing in vivo bone dynamics at the local trabecular level. The method does not require an a priori understanding of the diseases state, and can provide information about the trends of the bone remodeling process that may be used for better monitoring and treatment of diseases such as osteoporosis.