Cardiac hemodynamics computational modeling including chordae tendineae, papillaries, and valves dynamics

In the context of dynamic image-based computational fluid dynamics (DIB-CFD) modeling of cardiac system, the role of sub-valvular apparatus (chordae tendineae and papillary muscles) and the effects of different mitral valve (MV) opening/closure dynamics, have not been systemically determined. To provide a partial filling of this gap, in this study we performed DIB-CFD numerical experiments in the left ventricle, left atrium and aortic root, with the aim of highlighting the influence on the numerical results of two specific modeling scenarios: i) the presence of the sub-valvular apparatus, consisting of chordae tendineae and papillary muscles; ii) different MV dynamics models accounting for different use of leaflet reconstruction from imaging. This is performed for one healthy and one MV regurgitant subjects. Specifically, a systolic wall motion is reconstructed from time-resolved Cine-MRI images and imposed as boundary condition for the CFD numerical simulation. Analyzing the numerical results, we found that sub-valvular apparatus do not affect the global fluid dynamics quantities, although it creates local variations, such as the developing of vortexes or flow disturbances, which lead to different stress distributions on cardiac structures. Moreover, different MV dynamics are considered starting from Cine-MRI MV segmentation at different temporal configurations, and then they are compared and managed numerically through a resistive approach. The obtained results highlight the importance of including a sophisticated diastolic model of MV dynamics, which accounts for MV geometries during diastasis and A-wave, in terms of describing the disturbed flow and ventricular turbulence. ### Competing Interest Statement AC and CV are members of the INdAM group GNCS "Gruppo Nazionale per il Calcolo Scientifico" (National Group for Scientific Computing). CV has been partially supported by the Italian Ministry of University and Research (MIUR) within the PRIN (Research projects of relevant national interest) MIUR PRIN22-PNRR n. P20223KSS2 "Machine learning for fluid-structure interaction in cardiovascular problems: efficient solutions, model reduction, inverse problems", and by the Italian Ministry of Health within the PNC PROGETTO HUB LIFE SCIENCE - DIAGNOSTICA AVANZATA (HLS-DA) "INNOVA", PNC-E3-2022-23683266?CUP: D43C22004930001, within the "Piano Nazionale Complementare Ecosistema Innovativo della Salute" - Codice univoco investimento: PNC-E3-2022-23683266.