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Model order reduction for left ventricular mechanics via congruency training

View ORCID ProfilePaolo Di Achille, View ORCID ProfileJaimit Parikh, Svyatoslav Khamzin, Olga Solovyova, James Kozloski, View ORCID ProfileViatcheslav Gurev
doi: https://doi.org/10.1101/694075
Paolo Di Achille
1Healthcare and life sciences research, IBM TJ Watson research center, IBM research, Yorktown Heights, NY, USA
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Jaimit Parikh
1Healthcare and life sciences research, IBM TJ Watson research center, IBM research, Yorktown Heights, NY, USA
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Svyatoslav Khamzin
2Ural Federal University, Yekaterinburg, Russia
3Institute of Immunology and Physiology, Ural Branch of the Russian Academy of Sciences (UB RAS), Yekaterinburg, Russia
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Olga Solovyova
2Ural Federal University, Yekaterinburg, Russia
3Institute of Immunology and Physiology, Ural Branch of the Russian Academy of Sciences (UB RAS), Yekaterinburg, Russia
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James Kozloski
1Healthcare and life sciences research, IBM TJ Watson research center, IBM research, Yorktown Heights, NY, USA
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Viatcheslav Gurev
1Healthcare and life sciences research, IBM TJ Watson research center, IBM research, Yorktown Heights, NY, USA
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  • For correspondence: vgurev@us.ibm.com
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Abstract

Computational models of the cardiovascular system and heart function are currently being investigated as analytic tools to assist medical practice and clinical trials. Recent technological advances allow for finite element models of heart ventricles and atria to be customized to medical images and to assimilate electrical and hemodynamic measurements. Optimizing model parameters to physiological data is, however, challenging due to the computational complexity of finite element models. Metaheuristic algorithms and other optimization strategies typically require sampling hundreds of points in the model parameter space before converging to optimal solutions. Similarly, resolving uncertainty of model outputs to input assumptions is difficult for finite element models due to their computational cost. In this paper, we present a novel, multifidelity strategy for model order reduction of 3-D finite element models of ventricular mechanics. Our approach is centered around well established findings on the similarity between contraction of an isolated muscle and the whole ventricle. Specifically, we demonstrate that simple linear transformations between sarcomere strain (tension) and ventricular volume (pressure) are sufficient to reproduce global pressure-volume outputs of 3-D finite element models even by a reduced model with just a single myocyte unit. We further develop a procedure for congruency training of a surrogate low-order model from multi-scale finite elements, and we construct an example of parameter optimization based on medical images. We discuss how the presented approach might be employed to process large datasets of medical images as well as databases of echocardiographic reports, paving the way towards application of heart mechanics models in the clinical practice.

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  • The supplemental material was added.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted July 10, 2019.
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Model order reduction for left ventricular mechanics via congruency training
Paolo Di Achille, Jaimit Parikh, Svyatoslav Khamzin, Olga Solovyova, James Kozloski, Viatcheslav Gurev
bioRxiv 694075; doi: https://doi.org/10.1101/694075
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Model order reduction for left ventricular mechanics via congruency training
Paolo Di Achille, Jaimit Parikh, Svyatoslav Khamzin, Olga Solovyova, James Kozloski, Viatcheslav Gurev
bioRxiv 694075; doi: https://doi.org/10.1101/694075

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