Image-driven Stochastic Identification of Boundary Conditions for Predictive Simulation

Abstract : 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 present a novel image-driven method for fast identification of boundary conditions which are modelled 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 is 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.
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Igor Peterlik, Nazim Haouchine, Lukáš Ručka, Stéphane Cotin. Image-driven Stochastic Identification of Boundary Conditions for Predictive Simulation. 20th International Conference on Medical Image Computing and Computer Assisted Intervention, Sep 2017, Québec, Canada. ⟨hal-01570811⟩

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