Model of Vertebral Trabecular Anisotropy From MicroCT
Spine-related disorders are amongst the most fre-quently encountered problems in clinical medicine. For several applications such as 1) to improve the assessment of the strength of the spine, as well as 2) to optimize the personalization of spinal interventions, image-based biomechanical modeling of the verte-brae is expected to play an important predictive role. However, this requires the construction of computational models that are subject-specific and comprehensive. In particular, they need to incorporate information about the vertebral anisotropic micro-architecture, which plays a central role in the biomechanical function of the vertebrae. In practice, however, accurate person-alization of the vertebral trabeculae has proven to be difficult as its imaging in vivo is currently infeasible.
Consequently, this pa-per presents a statistical approach for accurate prediction of the vertebral fabric tensors based on a training sample of ex vivo mi-cro-CT images. To the best of our knowledge, this is the first pre-dictive model proposed and validated for vertebral datasets. The method combines features selection and partial least squares re-gression in order to derive optimal latent variables for the predic-tion of the fabric tensors based on the more easily extracted shape and density information. Detailed validation with 20 ex vivo T12 vertebrae demonstrates the accuracy and consistency of the ap-proach for the personalization of trabecular anisotropy.
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