The Cardiac Atlas Project

描述（申请人提供）：心脏畸形是最常见的出生缺陷类型。复杂先天性心脏病（CHD）治疗的改善已经使90%的先天性冠心病患者能够存活到成年早期。仅在美国，患有冠心病的成年人（约100万人）就比儿童多。这些患者中的许多人都有心室扩张和功能障碍的危险，特别是那些单心室功能正常的患者。预测那些将发展为适应性重构不良的患者和何时是困难的，但是医学图像中有大量潜在的有价值的信息，特别是纵向MRI检查。该项目的目标是利用基于心脏图谱的MR图像衍生的心室形状和生物力学参数模型，识别具有单心室生理学的冠心病患者代偿性或适应性不良重构的新的早期标志物，并通过“心脏图谱项目”（CAP）数据库部署这些模型和数据。CAP是一个全球性的联盟和在线资源，用于整合和共享心脏成像检查，以及参数模型衍生的功能分析和相关临床信息。我们的多学科专家团队在儿科心脏病学、医学成像、计算建模和医学信息学方面的目标是将这些资源扩展到冠心病患者，并使用这些模型来识别早期几何和生物力学预测不适应重构和心力衰竭。该项目将开发计算建模工具，以促进跨区域心脏形状和机械特征人口群体的统计分析。模型和相关的本体图式也将促进不同成像协议和模式之间的数据融合。该项目将与加州大学圣地亚哥分校的国家生物计算中心iDASH（整合数据分析、匿名化和共享）合作，并将使用和扩展iDASH开发的数据共享基础设施。具体目的是：(1)建立一个60例单心室病理的数据库，包括一些纵向随访。现有的CAP和iDASH基础设施将得到扩展，以容纳冠心病患者的纵向数据，以便深入了解疾病的进展和衰竭的发生；(2)识别形状相关物

DESCRIPTION (provided by applicant): Cardiac malformations are the most common type of birth defect. Improvements in the management of complex congenital heart disease (CHD) have resulted in >90% of those born with CHD now able to survive into early adulthood. In the U.S. alone, there are more adults with CHD (~1 million individuals) than children. Many of these patients are at risk of ventricular dilatation and dysfunction especially those with a functional single ventricle. Predicting those patients who will develop maladaptive remodeling and when is difficult, but there is a wealth of potentially valuable information available in medical images, especially longitudinal MRI exams. The goal of this project is to identify new early markers of compensatory or maladaptive remodeling in CHD patients with single ventricle physiology using atlas-based MR image-derived parametric models of ventricular shape and biomechanics, and to deploy these models and data via the "Cardiac Atlas Project" (CAP) database. CAP is a worldwide consortium and online resource for integrating and sharing cardiac imaging examinations, together with parametric model-derived functional analyses and associated clinical information. Our multi-disciplinary team of experts in pediatric cardiology, medical imaging, computational modeling and medical informatics aims to extend this resource to patients with CHD and use the models to identify early geometric and biomechanical predictors of maladaptive remodeling and heart failure. The project will develop computational modeling tools to facilitate statistical analysis across population groups of regional heart shape and mechanical characteristics. The models and associated ontological schema will also facilitate data fusion between different imaging protocols and modalities. This project will be a collaboration with iDASH (Integrating Data for Analysis, Anonymization, and Sharing), a National Center for Biological Computing at UCSD, and will use and extend the data-sharing infrastructure developed by iDASH. The specific aims are: (1) To create a database of 60 single ventricle pathologies including some with longitudinal follow-ups. Existing CAP and iDASH infrastructure will be extended to accommodate longitudinal data from CHD patients, to enable insights into the progression of disease and the onset of failure; (2) To identify shape correlates of mechanical dysfunction in single ventricle CHD by developing atlas-based image- derived statistical models of ventricular geometry, wall motion and changes over time; (3) To use the new atlas-based models of CHD to implement computational simulations of cardiac mechanics and fluid structure interactions in these pathologies to derive potential early markers of maladaptive remodeling. This work will have a significant impact on congenital cardiology by providing non-invasive analyses of regional shape, mechanics and bloodflow. If successful, the requirement for additional invasive procedures, which have increased risk of adverse events, could be substantially reduced. A web-accessible database will also facilitate education and training in cardiology, radiology, physiology and computational biology.