  Adult spine deformity (ASD) is prevalent and leads to a sagittal misalignment
in the vertebral column. Computational methods, including Finite Element (FE)
Models, have emerged as valuable tools for investigating the causes and
treatment of ASD through biomechanical simulations. However, the process of
generating personalized FE models is often complex and time-consuming. To
address this challenge, we present a repository of FE models with diverse spine
morphologies that statistically represent real geometries from a cohort of
patients. These models are generated using EOS images, which are utilized to
reconstruct 3D surface spine models. Subsequently, a Statistical Shape Model
(SSM) is constructed, enabling the adaptation of a FE hexahedral mesh template
for both the bone and soft tissues of the spine through mesh morphing. The SSM
deformation fields facilitate the personalization of the mean hexahedral FE
model based on sagittal balance measurements. Ultimately, this new hexahedral
SSM tool offers a means to generate a virtual cohort of 16807 thoracolumbar FE
spine models, which are openly shared in a public repository.
