Histomechanical characterization and microstructure-based modeling of right ventricular myocardium



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Right ventricular histomechanics have been historically overlooked, thus limiting our ability to describe the mechanisms underlying severe pathological conditions of the right heart. In this dissertation we set out to investigate the histomechanics of the right ventricular myocardium both in health and disease (pulmonary arterial hypertension), using a large animal (ovine) model. To this end, we combine mechanical testing, histology analysis, magnetic resonance imaging and microstructure-based modeling. Our computational approach is threefold, involving established homogenized models, novel machine learning metamodels and the use of embedded, discrete fiber networks. First, we found that the right ventricular myocardium in health exhibits nonlinear, anisotropic mechanical response. The homogenized models successfully captured this behavior at the cost of considerable computational time, subsequently accelerated by the machine learning metamodels. Moreover, we found that pulmonary arterial hypertension induced extracellular collagen deposition, spatially-dependent wall thickening, and increased stiffness at the low strain regime. Our embedded fiber network approach was able to account for these remodeling effects. Finally, throughout this work we have been making our experimental data and computational implementations publicly available, establishing for the first time a complete pipeline for the characterization of the right ventricular myocardium.


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