Abstract
Large-scale population-based imaging studies of preclinical and clinical heart disease are becoming possible due to the advent of standardized robust non-invasive imaging methods and infrastructure for big data analysis. This gives an exciting opportunity to gain new information about the development and progression of heart disease across population groups. However, the large amount of image data and prohibitive time required for image analysis present challenges for obtaining useful derived data from the images. Automated analysis tools for cardiac image analysis are only now becoming available. This paper reviews the challenges and possible solutions to the analysis of big imaging data in population studies. We also highlight the potential of recent large epidemiological studies using cardiac imaging to discover new knowledge on heart health and well-being.
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Acknowledgments
The Cardiac Atlas project was supported by Award Number R01HL087773 from the National Heart, Lung, and Blood Institute, NIH.
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Pau Medrano-Gracia and Avan Suinesiaputra declare that they have no conflict of interest.
Alistair A. Young and Brett R. Cowan report personal fees from Siemens Healthcare but report no overlap with the current work.
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This article does not contain any studies with human or animal subjects performed by any of the authors.
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This article is part of the Topical Collection on Cardiac PET, CT, and MRI
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Medrano-Gracia, P., Cowan, B.R., Suinesiaputra, A. et al. Challenges of Cardiac Image Analysis in Large-Scale Population-Based Studies. Curr Cardiol Rep 17, 9 (2015). https://doi.org/10.1007/s11886-015-0563-2
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DOI: https://doi.org/10.1007/s11886-015-0563-2