Department of Anesthesiology and Critical Care Medicine, Children's Hospital of Philadelphia, Philadelphia, PA 19104.
Department of Biomedical Engineering, Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT 84112.
J Biomech Eng. 2022 Oct 1;144(10). doi: 10.1115/1.4054485.
Atrioventricular valve regurgitation is a significant cause of morbidity and mortality in patients with acquired and congenital cardiac valve disease. Image-derived computational modeling of atrioventricular valves has advanced substantially over the last decade and holds particular promise to inform valve repair in small and heterogeneous populations, which are less likely to be optimized through empiric clinical application. While an abundance of computational biomechanics studies has investigated mitral and tricuspid valve disease in adults, few studies have investigated its application to vulnerable pediatric and congenital heart populations. Further, to date, investigators have primarily relied upon a series of commercial applications that are neither designed for image-derived modeling of cardiac valves nor freely available to facilitate transparent and reproducible valve science. To address this deficiency, we aimed to build an open-source computational framework for the image-derived biomechanical analysis of atrioventricular valves. In the present work, we integrated an open-source valve modeling platform, SlicerHeart, and an open-source biomechanics finite element modeling software, FEBio, to facilitate image-derived atrioventricular valve model creation and finite element analysis. We present a detailed verification and sensitivity analysis to demonstrate the fidelity of this modeling in application to three-dimensional echocardiography-derived pediatric mitral and tricuspid valve models. Our analyses achieved an excellent agreement with those reported in the literature. As such, this evolving computational framework offers a promising initial foundation for future development and investigation of valve mechanics, in particular collaborative efforts targeting the development of improved repairs for children with congenital heart disease.
房室瓣反流是获得性和先天性心脏瓣膜病患者发病率和死亡率的重要原因。过去十年，房室瓣的基于影像的计算模型已取得重大进展，尤其有望为小群体和异质人群的瓣膜修复提供信息，而这些人群不太可能通过经验性临床应用得到优化。虽然有大量的计算生物力学研究调查了成人的二尖瓣和三尖瓣疾病，但很少有研究调查其在易受影响的儿科和先天性心脏人群中的应用。此外，迄今为止，研究人员主要依赖于一系列商业应用程序，这些应用程序既不是为心脏瓣膜的基于影像的建模而设计的，也无法免费获得，从而无法实现瓣膜科学的透明和可重复。为了解决这一不足，我们旨在建立一个用于房室瓣基于影像的生物力学分析的开源计算框架。在本工作中，我们整合了一个开源瓣膜建模平台 SlicerHeart 和一个开源生物力学有限元建模软件 FEBio，以促进基于影像的房室瓣模型创建和有限元分析。我们进行了详细的验证和敏感性分析，以证明这种建模在应用于三维超声心动图衍生的儿科二尖瓣和三尖瓣模型时的准确性。我们的分析与文献报道的结果非常吻合。因此，这个不断发展的计算框架为未来的瓣膜力学研究和开发提供了一个有前景的初步基础，特别是针对为先天性心脏病儿童开发改进修复的合作努力。
