Data-Driven Automation of Patient-Specific Finite Element Modeling for TAVR

项目概要/摘要 经导管主动脉瓣置换术（TAVR）是治疗主动脉瓣狭窄的一种新兴治疗选择， 常见的心脏瓣膜疾病在美国每年导致约 15,000 人死亡。 TAVR 一直在稳步发展 自 2011 年以来越来越受欢迎，目前在美国每年执行超过 70,000 次 有限元 (FE) 通过模拟生物力学，方法显示出改善 TAVR 治疗计划的巨大潜力 解剖结构和部署的假肢装置之间的相互作用。然而，有限元方法是 目前严重受到患者特定几何形状的描绘过程的限制，因为手动描绘 3D CT 图像非常耗时且容易出错。已经提出了自动化方法，但是 由于对输入和输出特性的广泛假设，它们的适应性有限。这是 当需要扩展患者特定的几何形状来模拟各种情况时，尤其成问题 TAVR 的并发症。为了解决这些限制，该提案旨在快速、稳健且轻松地开发 适应性强的深度学习算法，用于根据 3D CT 自动描绘患者特定的几何形状 图像。目标 1 是开发基于模板变形的弱监督深度学习算法 描绘 TAVR 相关解剖结构，如左上心室心肌、主动脉瓣、 冠状动脉和升主动脉。模板变形策略将建立网格对应关系 在所有预测的体积有限元输出之间，弱监督将允许对复杂的模型进行建模 具有最低限度的专家标签的输出几何图形。目标 2 是整合解剖学上一致的 使用多任务深度学习钙化到最终网格输出。基于先前的医学知识 由于钙化应始终靠近解剖表面，目标 2 的主要目标是 鼓励有效共享目标 1 的成像特征以定位钙化。新颖的损失 作为这一目标的一部分，还将开发解剖学一致性。成功完成本提案后， 最终的统一深度学习模型将能够使用术前 3D CT 图像来生成功能齐全的 患者特定的体积 FE 网格，可实现精确且多功能的 TAVR 模拟，速率约为 20ms/ 图像。与当前工作流程相比，这加快了几个数量级，因此将 显着加速生物力学研究并使有限元模拟更接近临床应用。这项工作将是 在 James Duncan 博士的指导下，在耶鲁大学生物医学工程系进行 孙伟博士获得F31奖学金。培训将包括在交叉领域进行广泛的研究 生物医学图像分析、生物力学和机器学习，重点是有影响力的临床 应用程序。

PROJECT SUMMARY/ABSTRACT Transcatheter Aortic Valve Replacement (TAVR) is an emerging treatment option for aortic stenosis, a common heart valve disease that causes about 15,000 deaths per year in the U.S. TAVR has been steadily gaining popularity since 2011, and is now performed over 70,000 times per year in the U.S. Finite element (FE) methods have shown great potential for improving TAVR treatment planning by simulating the biomechanical interactions between anatomical structures and deployed prosthetic devices. However, FE methods are currently severely limited by the delineation process of patient-specific geometry, as manual delineation from 3D CT images is extremely time consuming and error-prone. Automated methods have been proposed, but they have limited adaptability due to extensive assumptions about input and output characteristics. This is especially problematic when extensions of patient-specific geometry are required to simulate various complications of TAVR. To address these limitations, this proposal aims to develop fast, robust, and easily adaptable deep learning algorithms for automating the delineation of patient-specific geometry from 3D CT images. Aim 1 is to develop template deformation-based weakly supervised deep learning algorithms to delineate TAVR-relevant anatomical structures such as the upper left ventricular myocardium, aortic valve, coronary arteries, and ascending aorta. The template deformation strategy will establish mesh correspondence between all predicted volumetric FE outputs, and weak supervision will allow for modeling of the complex output geometry with minimally sufficient expert labeling. Aim 2 is to incorporate anatomically consistent calcification to the final mesh outputs using multi-task deep learning. Based on prior medical knowledge that calcification should always be in close proximity to anatomical surfaces, the main goal for Aim 2 is to encourage effective sharing of imaging features from Aim 1 to also locate calcification. A novel loss for anatomical consistency will also be developed as part of this aim. Upon successful completion of this proposal, the final unified deep learning model will be able to use pre-operative 3D CT images to generate fully functional patient-specific volumetric FE meshes for accurate and versatile TAVR simulations, at a rate of ~20ms per image. This is a speed-up of several orders of magnitude compared to the current workflow, and thus will significantly accelerate biomechanics studies and bring FE simulations closer to clinical use. This work will be conducted at Yale University’s Biomedical Engineering department with guidance from Dr. James Duncan and Dr. Wei Sun under the F31 fellowship. The training will include extensive research at the intersection of biomedical image analysis, biomechanics, and machine learning, with emphasis on impactful clinical applications.