A finite element model of the cardiac ventricles with coupled circulation: Biventricular mesh generation with hexahedral elements, airbags and a functional mockup interface to the circulation

Zhang, Yue; Adams, Jennifer; Wang, Vicky Y.; Horwitz, Lucas; Tartibi, Mehrzad; Morgan, Ashley E.; Kim, Jiwon; Wallace, Arthur W.; Weinsaft, Jonathan W.; Ge, Liang; Ratcliffe, Mark B.

doi:10.1016/j.compbiomed.2021.104840

Cited by 4 publications

(2 citation statements)

References 49 publications

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“…Computational models have been created based on 2-and 3-dimensional echocardiography, as well as magnetic resonance imaging and computed tomography. These have generated biomechanical data on the healthy and diseased MV-LV relationship and different surgical techniques [17][18][19][20][21][22][23][24]. Furthermore, the models have predicted the observed role of MV elongation and papillary muscle anterior malposition in altering fluid interactions with the MV and driving LV outflow tract obstruction in hypertrophic cardiomyopathy [25,26].…”

Section: Biomechanical Foundations Of Mitral-ventricular Interaction ...mentioning

confidence: 99%

Utilization of Engineering Advances for Detailed Biomechanical Characterization of the Mitral–Ventricular Relationship to Optimize Repair Strategies: A Comprehensive Review

et al. 2023

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The geometrical details and biomechanical relationships of the mitral valve–left ventricular apparatus are very complex and have posed as an area of research interest for decades. These characteristics play a major role in identifying and perfecting the optimal approaches to treat diseases of this system when the restoration of biomechanical and mechano-biological conditions becomes the main target. Over the years, engineering approaches have helped to revolutionize the field in this regard. Furthermore, advanced modelling modalities have contributed greatly to the development of novel devices and less invasive strategies. This article provides an overview and narrative of the evolution of mitral valve therapy with special focus on two diseases frequently encountered by cardiac surgeons and interventional cardiologists: ischemic and degenerative mitral regurgitation.

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Section: Biomechanical Foundations Of Mitral-ventricular Interaction ...mentioning

confidence: 99%

Utilization of Engineering Advances for Detailed Biomechanical Characterization of the Mitral–Ventricular Relationship to Optimize Repair Strategies: A Comprehensive Review

et al. 2023

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“…ECGI can reconstruct cardiac through simulation models [11]- [13] , and using the 7 simulated data to construct mapping relationship 8 through regularization methods, machine 9 learning, and deep learning techniques [14], [15] . although very detailed, are still limited to being ventricular-only [16] or atrial-only [17] models and require significant computational time as well as high-performance software and computers.…”

mentioning

confidence: 99%

Ex Vivo Noninvasive Electrophysiologic Imaging Based on Cardiac Electrophysiology Simulation and Clinical Data

Chang,

Dong,

Fan

et al. 2024

Preprint

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Background: The risk stratification and prognosis of cardiac arrhythmia depend on the individual condition of patients, while invasive diagnostic methods may be risky to patient health, and current non-invasive diagnostic methods are applicable to few disease types without sensitivity and specificity. Cardiac electrophysiologic imaging technology reflects cardiac activities accurately and non-invasively, which is of great significance for the diagnosis and treatment of cardiac diseases. Methods: A complete three-dimensional bidomain cardiac electrophysiologic activity model was constructed, and simulated electrocardiogram data were obtained as training samples. Particle swarm optimization-back propagation neural network, convolutional neural network, and long short-term memory network were used respectively to reconstruct the cardiac surface potential. Results: The conduction sequence of simulation model is basically consistent with that of human heart. The P waves, PR intervals, QRS complex, and T waves in the simulated waveforms were within the normal clinical range, and the distribution trend of the simulated body surface potential mapping was consistent with the clinical data. The coefficient of determination R2 between the reconstruction results of all the algorithms and the true value is above 0.73, and the mean absolute error is below 14 mV, among which the R2 of long short-term memory network is about 0.97 and the mean absolute error about 1.2 mV. Conclusions: The electrophysiologic model constructed in this study can reflect the cardiac electrical activity, and basically contains the mapping relationship between the cardiac potential and the body surface potential. In cardiac potential reconstruction, long short-term memory network has significant advantages over other algorithms.

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Multiscale characterization of left ventricle active behavior in the mouse

et al. 2023

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A finite element model of the cardiac ventricles with coupled circulation: Biventricular mesh generation with hexahedral elements, airbags and a functional mockup interface to the circulation

Cited by 4 publications

References 49 publications

Utilization of Engineering Advances for Detailed Biomechanical Characterization of the Mitral–Ventricular Relationship to Optimize Repair Strategies: A Comprehensive Review

Utilization of Engineering Advances for Detailed Biomechanical Characterization of the Mitral–Ventricular Relationship to Optimize Repair Strategies: A Comprehensive Review

Ex Vivo Noninvasive Electrophysiologic Imaging Based on Cardiac Electrophysiology Simulation and Clinical Data

Multiscale characterization of left ventricle active behavior in the mouse

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