New 3D and 4D Image-Based Clinical Measures of Atrial Structural and Functional Remodeling in Atrial Fibrillation

项目摘要 该建议的重点是开发新的心房形状临床测量方法，以便更好地个性化 患者护理的房颤和先进的最先进的计算方法的统计 临床图像数据中的形状分析。心房颤动（AF）是成人中最常见的心律失常， 美国每年有超过40万人入院，美国每年的费用为60 - 70亿美元， 死亡风险增加两倍。我们将开发和测试新的基于心脏形状的房颤预测因子 大规模患者人群的结局，包括参加全额资助的多中心DECAAF II的患者 临床试验该提案将通过以下方式影响房颤患者的临床科学和护理标准：（1） 开发一种新的基于MRI的动态心脏形状和相关变形的测量方法， 心动周期和导管消融后心房动态（4D）结构变化的研究;（2） 房颤治疗结局和卒中风险因素的基于形状的预测因子的开发;以及（3） DECAAF II中心房的形状分析和基于形状的预测指数的前瞻性评价 临床试验人群。这项工作的技术贡献将影响生物医学形状领域 通过开发时空（4D）解剖形状分析方法和 为高度可变的心脏解剖结构开发更强大的建模算法。这个项目 结合了犹他州大学的临床经验和研究基础设施， 心律失常研究和管理（CARMA）中心拥有计算资源和专业知识， 科学计算和成像（SCI）研究所，并利用我们独特的访问大量的MRI 来自参加多中心DECAAF II临床试验的AF患者的图像数据。我们的团队由一个 计算机科学家，电生理学家和MRI物理学家的完美组合。

Project Summary This proposal focuses on the development of new clinical measures of atrial shape for better individualized patient care of atrial fibrillation and advances the state-of-the art in computational methods for the statistical analysis of shape in clinical image data. Atrial fibrillation (AF) is the most common cardiac arrhythmia in adults, with over 400,000 U.S. hospital admissions per year, annual costs in the U.S. of $6-7 billion, and an almost two-fold increase in the risk of mortality. We will develop and test new cardiac shape-based predictors of AF outcomes in a large patient population, including patients enrolled in the fully-funded multicenter DECAAF II clinical trial. This proposal will impact clinical science and standards of care for AF patients through (1) the development of a new MRI-based measure of dynamic cardiac shape and associated deformations over the cardiac cycle and investigation of the dynamic (4D) structural changes to the atria after catheter ablation; (2) the development of shape-based predictors of AF treatment outcomes and risk factors for stroke; and (3) shape analysis of the atria and prospective evaluation of shape-based predictive indices in the DECAAF II clinical trial population. Technological contributions of this work will impact the field of biomedical shape analysis through the development of methods for analysis of spatiotemporal (4D) anatomical shape and the development of more powerful modeling algorithms for the highly variable anatomy of the heart. This project combines the clinical experience and research infrastructure of the University of Utah's Comprehensive Arrhythmia Research and Management (CARMA) Center with the computational resources and expertise of the Scientific Computing and Imaging (SCI) Institute and leverages our unique access to a large cohort of MRI image data from AF patients enrolled in the multicenter DECAAF II clinical trial. Our team consists of an accomplished mix of computer scientists, electrophysiologists, and MRI physicists.