Model order reduction for left ventricular mechanics via congruency training

Autoři: Paolo Di Achille aff001;  Jaimit Parikh aff001;  Svyatoslav Khamzin aff002;  Olga Solovyova aff002;  James Kozloski aff001;  Viatcheslav Gurev aff001
Působiště autorů: Healthcare and Life Sciences Research, IBM T.J. Watson Research Center, Yorktown Heights, NY, United States of America aff001;  Ural Federal University, Yekaterinburg, Russia aff002;  Institute of Immunology and Physiology, Ural Branch of the Russian Academy of Sciences (UB RAS), Yekaterinburg, Russia aff003
Vyšlo v časopise: PLoS ONE 15(1)
Kategorie: Research Article


Computational models of the cardiovascular system and specifically heart function are currently being investigated as analytic tools to assist medical practice and clinical trials. To achieve clinical utility, models should be able to assimilate the diagnostic multi-modality data available for each patient and generate consistent representations of the underlying cardiovascular physiology. While finite element models of the heart can naturally account for patient-specific anatomies reconstructed from medical images, optimizing the many other parameters driving simulated cardiac functions is challenging due to computational complexity. With the goal of streamlining parameter adaptation, 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.

Klíčová slova:

Cardiac ventricles – Ejection fraction – Finite element analysis – Heart failure – Hemodynamics – Magnetic resonance imaging – Muscle cells – Simulation and modeling


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