From Medical Images to Fast Computational Models of Heart Electromechanics: An Integrated Framework towards Clinical Use View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2013

AUTHORS

Oliver Zettinig , Tommaso Mansi , Bogdan Georgescu , Saikiran Rapaka , Ali Kamen , Jan Haas , Karen S. Frese , Farbod Sedaghat-Hamedani , Elham Kayvanpour , Ali Amr , Stefan Hardt , Derliz Mereles , Henning Steen , Andreas Keller , Hugo A. Katus , Benjamin Meder , Nassir Navab , Dorin Comaniciu

ABSTRACT

With the recent advances in computational power, realistic modeling of heart function within a clinical environment has come into reach. Yet, current modeling frameworks either lack overall completeness or computational performance, and their integration with clinical imaging and data is still tedious. In this paper, we propose an integrated framework to model heart electromechanics from clinical and imaging data, which is fast enough to be embedded in a clinical setting. More precisely, we introduce data-driven techniques for cardiac anatomy estimation and couple them with an efficient GPU (graphics processing unit) implementation of the orthotropic Holzapfel-Ogden model of myocardium tissue, a GPU implementation of a mono-domain electrophysiology model based on the Lattice-Boltzmann method, and a novel method to correctly capture motion during isovolumetric phases. Benchmark experiments conducted on patient data showed that the computation of a whole heart cycle including electrophysiology and biomechanics with mesh resolutions of around 70k elements takes on average 1min 10s on a standard desktop machine (Intel Xeon 2.4GHz, NVIDIA GeForce GTX 580). We were able to compute electrophysiology up to 40.5× faster and biomechanics up to 15.2× faster than with prior CPU-based approaches, which breaks ground towards model-based therapy planning. More... »

PAGES

249-258

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-642-38899-6_30

DOI

http://dx.doi.org/10.1007/978-3-642-38899-6_30

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1019415797


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