Model order reduction for left ventricular mechanics via congruency training.

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ID: 91506
2020
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Abstract
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.
Reference Key
di-achille2020modelplos Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Di Achille, Paolo;Parikh, Jaimit;Khamzin, Svyatoslav;Solovyova, Olga;Kozloski, James;Gurev, Viatcheslav;
Journal PloS one
Year 2020
DOI
10.1371/journal.pone.0219876
URL
Keywords
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