Towards an efficient data assimilation in physically-based medical simulations

PETERLÍK, Igor and Antonín KLÍMA. Towards an efficient data assimilation in physically-based medical simulations. Online. In Bin Ma et al. Proceedings of IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2015. Washington, D.C. , USA: IEEE, 2015, p. 1412-1419. ISBN 978-1-4673-6798-1. Available from: https://dx.doi.org/10.1109/BIBM.2015.7359884.

Basic information

Original name

Towards an efficient data assimilation in physically-based medical simulations

Name in Czech

Směrem k efektivní datové asimilaci medicínských simulací založených na fyzice

Authors

PETERLÍK, Igor (703 Slovakia, guarantor, belonging to the institution) and Antonín KLÍMA (203 Czech Republic, belonging to the institution)

Edition

Washington, D.C. , USA, Proceedings of IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2015, p. 1412-1419, 8 pp. 2015

Publisher

IEEE

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Other information

Language

English

Type of outcome

Stať ve sborníku

Field of Study

10201 Computer sciences, information science, bioinformatics

Country of publisher

United States of America

Confidentiality degree

není předmětem státního či obchodního tajemství

Publication form

electronic version available online

References:

URL

RIV identification code

RIV/00216224:14610/15:00086693

Organization unit

Institute of Computer Science

ISBN

978-1-4673-6798-1

DOI

http://dx.doi.org/10.1109/BIBM.2015.7359884

UT WoS

000377335600241

Keywords in English

Data Assimilation; Kalman Filtering; Non-linear elasticity; Finite Element Method; Patient-specific Modeling

Tags

firank_B, rivok

Tags

International impact, Reviewed

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Abstract

V originále

Computer simulation of soft tissues is rapidly be- coming an important aspect of medical training, pre-operative planning and intra-operative navigation. Whereas in medical training, generic models are usually employed, both planing and navigation require patient-specific modeling. However, creating a patient-specific model is a challenging task, as many of the mechanical parameters of the organ tissues are unknown. One way of addressing the issue is to extend the deterministic simulation by methods based on stochastic modeling. In this paper we focus on parameter estimation in models with large number of degrees of freedom based on a variant of Kalman filtering. The main contribution of the paper is a detailed description of an integration of two advanced concepts of numerical modeling: we employ a state-of-the-art method of data assimilation based on reduced-order Kalman filtering in order to perform parameter estimation of a finite-element model of non-linear elasticity used in medical simulations. In order to assess the method, we present a preliminary evaluation of the accuracy of the parameter estimation as well as the performance using synthetic data with added noise. We also evaluate the parallelized version of the prediction phase and finally we describe further perspectives which, as we believe, will bring the data assimilation of models with many parameters closer to the real-time processing.