Image-based modeling for better understanding and assessment of atherosclerotic plaque progression and vulnerability: Data, modeling, validation, uncertainty and predictions
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Kamm_Image-based.pdf
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Author(s)
Tang, Dalin
Yang, Chun
Zheng, Jie
Canton, Gador
Bach, Richard
Huang, Xueying
Hatsukami, Thomas S.
Zhu, Jian
Ma, Genshan
Maehara, Akiko
Date Issued
March 2014
Journal
Journal of Biomechanics
Publisher
Elsevier
Citation
Tang, Dalin, Roger D. Kamm, Chun Yang, Jie Zheng, Gador Canton, Richard Bach, Xueying Huang, et al. “Image-Based Modeling for Better Understanding and Assessment of Atherosclerotic Plaque Progression and Vulnerability: Data, Modeling, Validation, Uncertainty and Predictions.” Journal of Biomechanics 47, no. 4 (March 2014): 834–846.
Version
Author's final manuscript
Abstract
Medical imaging and image-based modeling have made considerable progress in recent years in identifying atherosclerotic plaque morphological and mechanical risk factors which may be used in developing improved patient screening strategies. However, a clear understanding is needed about what we have achieved and what is really needed to translate research to actual clinical practices and bring benefits to public health. Lack of in vivo data and clinical events to serve as gold standard to validate model predictions is a severe limitation. While this perspective paper provides a review of the key steps and findings of our group in image-based models for human carotid and coronary plaques and a limited review of related work by other groups, we also focus on grand challenges and uncertainties facing the researchers in the field to develop more accurate and predictive patient screening tools.
MIT Department
Massachusetts Institute of Technology. Department of Biological Engineering
Massachusetts Institute of Technology. Department of Mechanical Engineering
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Creative Commons Attribution-NonCommercial-NoDerivs License
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DOI of Published Version
https://doi.org/10.1016/j.jbiomech.2014.01.012
