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: 10683708
• 负责人: Daniel Pak
• 金额: $ 2.34万
• 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 2023-10-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10683708
• 关键词: 3-Dimensional; Acceleration; 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; 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; Time; Training; Universities; Variant; Ventricular; Work; X-Ray Computed Tomography; aortic valve; aortic valve replacement; ascending aorta; automated algorithm; 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术(TAVR)是一种新兴的治疗主动脉瓣狭窄的方法。 在美国，每年导致约15,000人死亡的常见心脏瓣膜疾病一直在稳步上升 自2011年以来越来越受欢迎，现在每年在美国有限元(FE)演出超过7万次 通过模拟生物力学的方法在改进TAVR治疗计划方面显示出巨大的潜力 解剖结构和已展开的假体装置之间的相互作用。然而，有限元方法是 目前严重受限于患者特定几何形状的描绘过程，如来自 三维CT图像非常耗时且容易出错。已经提出了自动化的方法，但 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 当需要扩展特定于患者的几何体来模拟各种 TAVR的并发症。为了解决这些限制，本提案旨在开发快速、健壮且容易的 自适应深度学习算法用于从3D CT自动描绘患者特定的几何图形 图像。目标1是开发基于模板变形的弱监督深度学习算法 勾勒出与TAVR相关的解剖结构，如左上室心肌、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 在所有预测的体积有限元输出和弱监督之间，将允许对综合体进行建模 输出具有最低限度的专家标签的几何图形。目标2是将解剖学上的一致性 利用多任务深度学习将钙化转化为最终的网格输出。基于先前的医学知识 钙化应始终靠近解剖表面，目标2的主要目标是 鼓励有效共享来自目标1的成像特征，以定位钙化。一种新的损失 作为这一目标的一部分，解剖学上的一致性也将得到发展。在成功完成本提案后， 最终的统一深度学习模型将能够使用术前3D CT图像生成完全功能的 患者特定的体积有限元网格，用于精确和通用的TAVR模拟，速率约为20ms/ 图像。与当前的工作流程相比，这是几个数量级的提速，因此将 显著加快生物力学研究，并使有限元模拟更接近临床使用。这项工作将是 在耶鲁大学生物医学工程系进行，詹姆斯·邓肯博士和 孙伟博士荣获F31奖学金。培训将包括在交叉点进行广泛的研究 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 申请。