We propose a dynamic-image driven computational approach for the modeling and simulation of the spine. We use static and dynamic medical images, computational methods and anatomic knowledge to accurately model and measure the subject-specific dynamic behavior of structures in the spine. The resulting models have applications in biomechanical simulations, computer animation, and orthopaedic surgery.
We first develop a semi-automated motion reconstruction method for measuring 3D motion with sub-millimeter accuracy. The automation of the method enables the study of subject-specific spine kinematics over large groups of population. The accuracy of the method enables the modeling and analysis of small anatomical features that are difficult to capture in-vivo using existing imaging techniques. We then develop a set of computational tools to model spine soft-tissue structures. We build dynamic-motion driven geometric models that combine the complementary strengths of the accurate but static models used in orthopaedics and the dynamic but low level-of-detail multibody simulations used in humanoid computer animation. Leveraging dynamic images and reconstructed motion, this approach allows the modeling and analysis anatomical features that are too small to be imaged in-vivo and of their dynamic behavior. Finally, we generate predictive, subject-specific models of healthy and symptomatic spines. The predictive models help to identify, understand and validate hypotheses about spine disorders
