Model order reduction for left ventricular mechanics via congruency training
| Autor: | Viatcheslav Gurev, Svyatoslav Khamzin, Paolo Di Achille, James R. Kozloski, Jaimit Parikh, Olga Solovyova |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Male
COMPUTER SIMULATION TENSION Computer science MODELS CARDIOVASCULAR Hemodynamics 02 engineering and technology 030204 cardiovascular system & hematology Ventricular Function Left Diagnostic Radiology HEART CONTRACTION 0302 clinical medicine Animal Cells HUMAN CELL Medicine and Health Sciences Contraction (operator theory) Model order reduction Computational model Multidisciplinary Ejection fraction Radiology and Imaging Simulation and Modeling Applied Mathematics Models Cardiovascular HUMAN Heart HUMANS Hematology Magnetic Resonance Imaging THREE DIMENSIONAL FINITE ELEMENT ANALYSIS MUSCLE FIBER CULTURE Finite element method Biomechanical Phenomena FEMALE Cardiovascular physiology Echocardiography Physical Sciences FINITE ELEMENT ANALYSIS Medicine SARCOMERES Female Anatomy Cellular Types ECHOCARDIOGRAPHY HEART LEFT VENTRICLE FUNCTION Algorithm AGED DIAGNOSTIC IMAGING Research Article Sarcomeres CLINICAL PRACTICE Similarity (geometry) Imaging Techniques Cardiac Ventricles Process (engineering) Heart Ventricles Science BIOLOGICAL MODEL CASE REPORT Finite Element Analysis PATHOPHYSIOLOGY 0206 medical engineering Cardiology Muscle Tissue Research and Analysis Methods HEART VENTRICLE 03 medical and health sciences Diagnostic Medicine MYOCARDIAL CONTRACTION Humans Computer Simulation ARTICLE PHYSIOLOGY Aged Heart Failure Muscle Cells MALE VENTRICULAR FUNCTION LEFT Biology and Life Sciences Cell Biology HEART LEFT VENTRICLE Function (mathematics) Myocardial Contraction BIOMECHANICS BIOMECHANICAL PHENOMENA 020601 biomedical engineering Biological Tissue SARCOMERE HEART FAILURE MECHANICS Cardiovascular Anatomy HEART VENTRICLES Mathematics Ejection Fraction |
| Zdroj: | PLoS ONE PLoS ONE, Vol 15, Iss 1, p e0219876 (2020) |
| Popis: | 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 multiscale 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. © 2020 Di Achille et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. 19-14- 00134 Russell Sage Foundation, RSF SK and OS were funded by RSF (http:// www.rscf.ru/en/) as described below. Part of this work was carried out within the framework of the IIF UrB RAS government assignment and was partially supported by the UrFU Competitiveness Enhancement Program (agreement 02. A03.21.0006) as well as the RSF grant (No. 19-14- 00134). The Uran supercomputer at IMM UrB RAS was used for part of the model calculations. IBM provided support in the form of salaries for authors PA, JP, JK and VG but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. The specific roles of these authors are articulated in the "author contributions" section. |
| Databáze: | OpenAIRE |
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