This project seeks to establish a structural and functional atlas of the heart. Initially comprising cardiac magnetic resonance imaging (MRI) examinations, together with derived functional analyses and associated clinical variables, the database will be extendible to allow inclusion of data from a variety of imaging and other sources. Cardiac MRI examinations provide detailed, quantitative data on heart structure and function, and standardized protocols are now routinely used in a number of studies. The initial goals of this project are to facilitate statistical analysis of regional heart shape and wall motion characteristics, across population groups, via the application of parametric mathematical modeling tools. This project will combine cardiac modeling and biophysical analysis methods developed by the University of Auckland with structural database and probabilistic mapping infrastructure developed by the UCLA Center for Computational Biology (CCB). The mission of the National Centers for Biological Computing collaboration program will be advanced by extending the application area of the CCB to another organ: the heart.
The specific aims of the project are to: 1) Establish a database of cardiac MRI studies of asymptomatic and symptomatic patients. Data from two large studies will be used to initiate the database: MESA (Multi-Ethnic Study of Atherosclerosis), consisting of 5,004 asymptomatic subjects, and DETERMINE (Defibrillators to Reduce Risk by Magnetic Resonance Imaging Evaluation), consisting of 10,000 patients with myocardial infarction. 2) Develop tools and procedures to enable cardiac MRI studies to be classified, labeled, and searched using standardized protocols. Procedures and ontologies for the characterization and classification of anatomical and physiological data will be extended to cardiac MRI examinations. 3) Develop downloadable software tools for the mapping of cardiac structure and function, transformation of results between studies, and the probabilistic evaluation of temporal abnormalities in regional heart wall motion, in relation to population subgroups. This project will significantly improve the evaluation of cardiac performance and disease processes, establish characteristic parameters of cardiac structure and function on a regional basis, and enable the evaluation of clinical cases in relation to the statistical distributions within patient subgroups.

Public Health Relevance

Heart disease is the number one killer in the USA and many other countries around the world. Significant time and money are spent researching heart function in health and disease using magnetic resonance imaging. By pooling data from many studies into a single resource, researchers will be able to perform standardized comparisons across many patient groups, in order to better predict and evaluate the effects of disease and treatment. ? ? ?

National Institute of Health (NIH)
National Heart, Lung, and Blood Institute (NHLBI)
Research Project (R01)
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Special Emphasis Panel (ZRG1-BST-E (51))
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Larkin, Jennie E
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University of Auckland
New Zealand
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Suinesiaputra, Avan; Ablin, Pierre; Alba, Xenia et al. (2018) Statistical shape modeling of the left ventricle: myocardial infarct classification challenge. IEEE J Biomed Health Inform 22:503-515
Piras, Paolo; Teresi, Luciano; Puddu, Paolo Emilio et al. (2017) Morphologically normalized left ventricular motion indicators from MRI feature tracking characterize myocardial infarction. Sci Rep 7:12259
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Suinesiaputra, Avan; Bluemke, David A; Cowan, Brett R et al. (2015) Quantification of LV function and mass by cardiovascular magnetic resonance: multi-center variability and consensus contours. J Cardiovasc Magn Reson 17:63
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Medrano-Gracia, Pau; Cowan, Brett R; Suinesiaputra, Avan et al. (2014) Atlas-based Anatomical Modeling and Analysis of Heart Disease. Drug Discov Today Dis Models 14:33-39
Medrano-Gracia, Pau; Cowan, Brett R; Ambale-Venkatesh, Bharath et al. (2014) Left ventricular shape variation in asymptomatic populations: the Multi-Ethnic Study of Atherosclerosis. J Cardiovasc Magn Reson 16:56

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