Publication details

Image-driven Stochastic Identification of Boundary Conditions for Predictive Simulation

Authors

PETERLÍK Igor HAOUCHINE Nazim RUČKA Lukáš COTIN Stéphane

Year of publication 2017
Type Article in Proceedings
Conference Medical Image Computing and Computer-Assisted Intervention - MICCAI 2017: 20th International Conference, Quebec City, QC, Canada, September 11-13, 2017, Proceedings, Part II
MU Faculty or unit

Faculty of Informatics

Citation
Web https://doi.org/10.1007/978-3-319-66185-8_62
Doi http://dx.doi.org/10.1007/978-3-319-66185-8_62
Keywords Boundary conditions Stochasric data assimilation Finite element method Surgical augmented reality Hepatic surgery
Description 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.
Related projects: