Image-driven Stochastic Identification of Boundary Conditions for Predictive Simulation

Varování

Publikace nespadá pod Filozofickou fakultu, ale pod Fakultu informatiky. Oficiální stránka publikace je na webu muni.cz.
Autoři

PETERLÍK Igor HAOUCHINE Nazim RUČKA Lukáš COTIN Stéphane

Rok publikování 2017
Druh Článek ve sborníku
Konference Medical Image Computing and Computer-Assisted Intervention - MICCAI 2017: 20th International Conference, Quebec City, QC, Canada, September 11-13, 2017, Proceedings, Part II
Fakulta / Pracoviště MU

Fakulta informatiky

Citace
www https://doi.org/10.1007/978-3-319-66185-8_62
Doi http://dx.doi.org/10.1007/978-3-319-66185-8_62
Klíčová slova Boundary conditions Stochasric data assimilation Finite element method Surgical augmented reality Hepatic surgery
Popis 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.
Související projekty:

Používáte starou verzi internetového prohlížeče. Doporučujeme aktualizovat Váš prohlížeč na nejnovější verzi.