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)治疗的改进使90%的先天先天性心脏病患者现在能够存活到成年早期。仅在美国，成人冠心病患者(约100万人)就比儿童多。这些患者中的许多人都有脑室扩张和功能障碍的风险，特别是那些功能正常的单脑室患者。预测哪些患者将发生适应性不良重塑以及何时发生是困难的，但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图谱的磁共振图像衍生的心室形态和生物力学参数模型，在单心室生理学的冠心病患者中识别新的代偿性或适应性不良重构的早期标志物，并通过“心脏图谱计划”(CAP)数据库部署这些模型和数据。CAP是一个全球联盟和在线资源，用于集成和共享心脏成像检查，以及基于参数模型的功能分析和相关的临床信息。我们的多学科专家团队由儿科心脏病学、医学成像、计算建模和医学信息学组成，旨在将这一资源扩展到CHD患者，并使用这些模型来识别早期的几何和生物力学预测因素，即适应性不良重塑和心力衰竭。该项目将开发计算建模工具，以促进对区域心脏形状和机械特征的人口群体进行统计分析。这些模型和相关的本体论模式还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迭戈分校的国家生物计算中心iDASH(集成数据进行分析、匿名和共享)合作，并将使用和扩展iDASH开发的数据共享基础设施。具体目标是：(1)建立一个包含60个单脑室病理的数据库，其中包括一些纵向随访。现有的CAP和iDASH基础设施将被扩展，以容纳来自CHD患者的纵向数据，从而能够深入了解疾病的进展和失败的发生；(2)识别形状相关 通过开发基于图谱的心室几何、室壁运动和随时间变化的基于图像的统计模型来研究单心室冠心病的机械功能障碍；(3)使用基于图谱的新的冠心病模型来对这些病理中的心脏力学和流体结构相互作用进行计算机模拟，以获得潜在的适应性不良重构的早期标志。这项工作将对先天性心脏病提供对区域形状、力学和血流的非侵入性分析，从而对先天性心脏病产生重大影响。如果成功，增加不良事件风险的额外侵入性手术的需求可能会大幅减少。一个可上网的数据库还将促进心脏病学、放射学、生理学和计算生物学方面的教育和培训。

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.