On the effects of leaflet microstructure and constitutive model on the closing behavior of the mitral valve
Recent long-term studies showed an unsatisfactory recurrence rate of severe mitral regurgitation 3–5 years after surgical repair, suggesting that excessive tissue stresses and the resulting strain-induced tissue failure are potential etiological factors controlling the success of surgical repair for treating mitral valve (MV) diseases. We hypothesized that restoring normal MV tissue stresses in MV repair techniques would ultimately lead to improved repair durability through the restoration of MV normal homeostatic state. Therefore, we developed a micro- and macro- anatomically accurate MV finite element model by incorporating actual fiber microstructural architecture and a realistic structure-based constitutive model. We investigated MV closing behaviors, with extensive in vitro data used for validating the proposed model. Comparative and parametric studies were conducted to identify essential model fidelity and information for achieving desirable accuracy. More importantly, for the first time, the interrelationship between the local fiber ensemble behavior and the organ-level MV closing behavior was investigated using a computational simulation. These novel results indicated not only the appropriate parameter ranges, but also the importance of the microstructural tuning (i.e., straightening and re-orientation) of the collagen/elastin fiber networks at the macroscopic tissue level for facilitating the proper coaptation and natural functioning of the MV apparatus under physiological loading at the organ level. The proposed computational model would serve as a logical first step toward our long-term modeling goal—facilitating simulation-guided design of optimal surgical repair strategies for treating diseased MVs with significantly enhanced durability.
KeywordsMapped fiber microstructural architecture Image-based FE simulation Simplified structural constitutive model Affine fiber kinematics In vitro validations
Support from the National Institutes of Health (NIH) Grants R01 HL119297, HL63954, HL103723, and HL73021 is greatly acknowledged. Dr. Chung-Hao Lee was supported in part by the American Heart Association (AHA) Postdoctoral Fellowship (14POST18160013) and a UT Austin ICES Postdoctoral Fellowship. The assistance from Ted Weber and Ronen G. Aniti for image segmentation and development of the MV finite element model is greatly appreciated.
Conflict of interest
None of the authors have a conflict of interests with the present work.
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