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Stochastic Identification of Boundary Conditions

In computer-aided interventions, biomechanical models reconstructed from the pre-operative data are used via augmented reality to facilitate the intra-operative navigation. The predictive power of such models highly depends on the knowledge of boundary conditions. However, in the context of patient-specific modeling, neither the pre-operative nor the intra-operative modalities provide a reliable information about the location and mechanical properties of the organ attachments. 
We have proposed a novel image-driven method for fast identification of boundary conditions which are modeled as stochastic parameters. The method employs the reduced-order unscented Kalman filter to transform in real- time the probability distributions of the parameters, given observations extracted from intra-operative images. The method has been evaluated using synthetic, phantom and real data acquired in vivo on a porcine liver. A quantitative assessment is presented and it is shown that the method significantly increases the predictive power of the biomechanical model.