Image-driven Stochastic Identification of Boundary Conditions for Predictive Simulation

Result code in 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>
Result on the web
<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>

Result language
angličtina
Original language name
Image-driven Stochastic Identification of Boundary Conditions for Predictive Simulation
Original language 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.
Czech name
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Czech description
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Publication year
2017
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
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
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Number of pages
9
Pages from-to
548-556
Publisher name
Springer
Place of publication
Cham
Event location
Quebec
Event date
Jan 1, 2017
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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