Human-Specific Prediction, Training, and Visualization Tools for the Tricuspid Valve from Existing Data

抽象的。大约有160万美国人患有严重的三尖瓣反流。标准 对于三尖瓣反流的治疗，外科手术是令人震惊的无效的，复发性反流在高达 30%的患者在几年内。我们假设，这些糟糕的结果是由于我们非常有限的基础 了解正常的瓣膜功能以及瓣膜潜在的、根本性的疾病和失效机制。 用于预测、训练和可视化的模型在外科手术的许多其他领域都很有价值，并且可以 成为克服我们目前对三尖瓣的认识空白的关键工具。然而，为数不多的 现有的三尖瓣模型是简化的、按人群平均的、不是特定于人类的或以上所有的。 为了填补工具中的这一明显空白，我们的目标是对以前在独立的、 人类的心脏在跳动。基于这些数据，我们将创建完全详细的、人类特有的三尖瓣模型 并将这些模型作为公开可用的预测、培训和可视化工具组合进行管理。对这件事 最后，我们将首先创建用于预测性模拟的人体三尖瓣的详细有限元模型。 具体地说，我们将建立和验证八个对象的高保真有限元模型，这些对象的传单和 环状几何形状、小叶和脊索的机械/微观结构特性以及边界条件都是 特定主题。我们将管理这些模型，使其与广受欢迎的、由NIH资助的和开放的- 源有限元软件FEBio。因为FEBio是一个研究代码，我们也将使我们的模型 与最广泛使用的商业有限元软件之一ABAQUS兼容。第二，我们将创造 用于实时训练模拟的人体三尖瓣的八个响应模型。为此，我们将 介绍了使用线性材料的三尖瓣的低保真但高效的计算模型 定律、降维、简化的解决方案策略，以及用于加速的GPU加速。这些型号 将比我们的有限元模型保真度低，但速度足够快，可以集成到一个流行的和开放的 实时手术模拟软件套件：SOFA。最后，我们还将创建八个增强现实 用于解剖可视化的人体三尖瓣模型。我们将制作这些动态的和有标签的模型 可通过增强现实网络界面公开访问，该界面将这些模型作为智能手机托管 临床医生、患者和工程师可访问的资源。一旦我们成功完成了这项工作，我们 将创建用于三尖瓣预测建模、培训和可视化的工具。所有工具都将被制造出来 通过知识共享署名-非商业性-Share Alike 4.0 International公开提供 驾照。总而言之，这些可获得的结果将克服我们目前在三尖瓣和三尖瓣方面的工具差距 解决高度普遍的、未得到满足的临床需求。

ABSTRACT. Roughly 1.6 million Americans suffer from significant tricuspid valve regurgitation. The standard treatment for tricuspid valve regurgitation, surgery, is shockingly ineffective with recurrent regurgitation in up to 30% of patients within a few years. We posit that these poor outcomes are due to our very limited basic understanding of normal valve function and the valve's underlying, fundamental disease and failure mechanisms. Models for prediction, training, and visualization have been valuable in many other areas of surgery and could be critical tools toward overcoming our current knowledge gaps about the tricuspid valve. However, the few existing models of the tricuspid valve are simplified, population-averaged, not human-specific, or all of the above. To fill this clear gap in tools, our goal is to do a secondary analysis on previous data collected on isolated, beating, human hearts. Based on these data, we will create fully-detailed, human-specific tricuspid valve models and curate these models as an openly available portfolio of prediction, training, and visualization tools. To this end, we will first create detailed finite element models of the human tricuspid valve for predictive simulations. Specifically, we will establish and validate high-fidelity finite element models of eight subjects whose leaflet and annular geometries, leaflet and chordae mechanical/microstructural properties, and boundary conditions are all subject-specific. We will curate these models to be compatible with the widely popular, NIH-funded, and open- source finite element software FEBio. Because FEBio is a research code, we will make our models also compatible with one of the most widely-used commercial finite element software, Abaqus. Second, we will create eight responsive models of the human tricuspid valve for real-time training simulations. To this end, we will introduce low-fidelity, but high-efficiency computational models of the tricuspid valve that use linear material laws, dimensional reduction, simplified solution strategies, and GPU acceleration for speed-up. These models will be of lower fidelity than our finite element models, but fast enough to be integrated into a popular and open software suite for real-time surgical simulations: SOFA. Finally, we will also create eight augmented reality models of the human tricuspid valve for anatomic visualization. We will make these dynamic and labelled models openly accessible through an augmented reality web interface that hosts these models as a smart-phone accessible resource for clinicians, patients, and engineers. Once we have successfully concluded this work, we will have created tools for tricuspid valve predictive modeling, training, and visualization. All tools will be made openly available through the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. Together, these accessible outcomes will overcome our current tool gap for the tricuspid valve and address a highly prevalent, unmet clinical need.