Computed tomography-based computational modeling offers a promising approach to representing bone tissue distribution; however, local structure-property mapping has largely relied on experiments that typically focus only on the centric region of the vertebra, often producing results that differ significantly. Here, we develop a data-driven framework that integrates imaging, experimental testing, and numerical modeling of human lumbar vertebrae to establish a unified density-modulus relationship across the entire vertebral body.
In bone biomechanics modeling, in silico models constructed from standard-resolution imaging lack adequate microstructural details and sufficient validation, whereas models built from high-resolution imaging demand excessive computational resources and prohibitively high radiation doses. Hence, we develop a data-driven multiscale modeling framework aimed at estimating human vertebral performance both accurately and efficiently, with strong potential for clinical translation.
