From medical images to fast computational models of heart electromechanics: An integrated framework towards clinical use
Zettinig O, Mansi T, Georgescu B, Rapaka S, Kamen A, Haas J, Frese KS, Sedaghat-Hamedani F, Kayvanpour E, Amr A, Hardt S, Mereles D, Steen H, Keller A, Katus HA, Meder B, Navab N, Comaniciu D (2013)
Publication Type: Conference contribution
Publication year: 2013
Journal
Book Volume: 7945 LNCS
Pages Range: 249-258
Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Event location: GBR
ISBN: 9783642388989
DOI: 10.1007/978-3-642-38899-6_30
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 40.5x faster and biomechanics up to 15.2x faster than with prior CPU-based approaches, which breaks ground towards model-based therapy planning. © 2013 Springer-Verlag.
Involved external institutions
Siemens Corporate Research
United States (USA) (US)
Universitätsklinikum Heidelberg
Germany (DE)
Technische Universität München (TUM)
Germany (DE)
United States (USA) (US)
Universitätsklinikum Heidelberg
Germany (DE)
Technische Universität München (TUM)
Germany (DE)
How to cite
APA:
Zettinig, O., Mansi, T., Georgescu, B., Rapaka, S., Kamen, A., Haas, J.,... Comaniciu, D. (2013). From medical images to fast computational models of heart electromechanics: An integrated framework towards clinical use. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 249-258). GBR.
MLA:
Zettinig, Oliver, et al. "From medical images to fast computational models of heart electromechanics: An integrated framework towards clinical use." Proceedings of the 7th International Conference on Functional Imaging and Modeling of the Heart, FIMH 2013, GBR 2013. 249-258.
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