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

TAVR 患者特定有限元建模的数据驱动自动化

• 批准号: 10386122
• 负责人: Daniel Pak
• 金额: $ 4.68万
• 依托单位: YALE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-03-01 至 2025-02-28
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10386122
• 关键词: 3-Dimensional; Address; Algorithms; Anatomic Models; Anatomic Surface; Anatomy; Aortic Valve Stenosis; Automation; Biomechanics; Biomedical Engineering; Cessation of life; Characteristics; Clinic; Clinical; Complex; Computational Technique; Consumption; Coronary artery; Data; Discipline; Elements; Engineering; Fellowship; Finite Element Analysis; Geometry; Goals; Heart Valve Diseases; Image; Image Analysis; Joints; Knowledge; Label; Lead; Left; Location; Machine Learning; Manuals; Medical; Methods; Modeling; Motivation; Myocardium; Operative Surgical Procedures; Outcome; Output; Patient imaging; Patient-Focused Outcomes; Patients; Prevalence; Process; Prosthesis; Research; Research Proposals; Shapes; Speed; Structure; Supervision; Techniques; The Sun; Time; Training; Universities; Variant; Ventricular; Work; X-Ray Computed Tomography; aortic valve; aortic valve replacement; ascending aorta; automated algorithm; base; biomedical imaging; calcification; clinical application; deep learning; deep learning algorithm; deep learning model; design; direct application; high risk; improved; interest; multitask; novel; population based; simulation; treatment planning; treatment strategy

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.

项目摘要/摘要 经导管主动脉瓣置换（TAVR）是主动脉狭窄的新兴治疗选择， 在美国，每年大约15,000人死亡的常见心脏瓣膜疾病一直在繁忙 自2011年以来越来越受欢迎，现在在美国有限元（FE）中每年进行70,000次超过70,000次 方法通过模拟生物力学显示了改善TAVR治疗计划的巨大潜力 解剖结构与部署的假肢设备之间的相互作用。但是，铁方法是 目前受患者特定几何形状的描述过程受到严格限制，作为手动描述 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征的广泛假设，它们具有有限的适应性。这是 尤其是有问题的当需要患者特定几何形状的扩展以模拟各种 塔夫的并发症。为了解决这些限制，该提案旨在快速，强大且轻松地发展 适应性深度学习算法，用于自动化3D CT的患者特定几何形状的描述 图像。目的1是开发基于模板变形的弱监督深度学习算法 描绘与TAVR相关的解剖结构，例如左上心肌，主动脉瓣， 冠状动脉，并升主动脉。模板变形策略将建立网格对应关系 在所有预测的体积Fe输出和弱监管之间，将允许对复合物进行建模 输出几何形状具有最小的专家标签。 AIM 2是结合解剖学一致 使用多任务深度学习钙化最终网格输出。基于先前的医学知识 该计算应始终靠近解剖表面，AIM 2的主要目标是 鼓励有效地共享AIM 1的成像功能，以定位钙化。一个新颖的损失 该目标的一部分也将开发解剖一致性。成功完成此建议后， 最终的统一深度学习模型将能够使用术前的3D CT图像来生成功能齐全的功能 精确和多功能TAVR模拟的患者特定体积Fe网格，每次约20ms 图像。与当前的工作流相比，这是几个数量级的加速度，因此 显着加速生物力学研究，并将FE模拟更接近临床使用。这项工作将是 詹姆斯·邓肯（James Duncan）博士的指导和 F31奖学金下的Wei Sun博士。培训将包括在 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 申请。