Image-driven Stochastic Identification of Boundary Conditions for Predictive Simulation

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14330%2F17%3A00097947" target="_blank" >RIV/00216224:14330/17:00097947 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.1007/978-3-319-66185-8_62" target="_blank" >https://doi.org/10.1007/978-3-319-66185-8_62</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-319-66185-8_62" target="_blank" >10.1007/978-3-319-66185-8_62</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Image-driven Stochastic Identification of Boundary Conditions for Predictive Simulation

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Image-driven Stochastic Identification of Boundary Conditions for Predictive Simulation

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10200 - Computer and information sciences

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2017

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Medical Image Computing and Computer-Assisted Intervention − MICCAI 2017: 20th International Conference, Quebec City, QC, Canada, September 11-13, 2017, Proceedings, Part II

ISBN

9783319661841

ISSN

0302-9743

e-ISSN

—

Počet stran výsledku

9

Strana od-do

548-556

Název nakladatele

Springer

Místo vydání

Cham

Místo konání akce

Quebec

Datum konání akce

1. 1. 2017

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

—