Abstract：
:Spine-related disorders are amongst the most frequently 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 vertebrae 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 personalization of the vertebral trabeculae has proven to be difficult as its imaging in vivo is currently infeasible. Consequently, this paper presents a statistical approach for accurate prediction of the vertebral fabric tensors based on a training sample of ex vivo micro-CT images. To the best of our knowledge, this is the first predictive model proposed and validated for vertebral datasets. The method combines features selection and partial least squares regression in order to derive optimal latent variables for the prediction 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 approach for the personalization of trabecular anisotropy.
journal_name
IEEE Trans Med Imagingjournal_title
IEEE transactions on medical imagingauthors
Lekadir K,Hoogendoorn C,Hazrati-Marangalou J,Taylor Z,Noble C,van Rietbergen B,Frangi AFdoi
10.1109/TMI.2014.2387114subject
Has Abstractpub_date
2015-08-01 00:00:00pages
1747-59issue
8eissn
0278-0062issn
1558-254Xjournal_volume
34pub_type
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