4.6 Article

Image-based biomechanical modeling for coronary atherosclerotic plaque progression and vulnerability prediction

期刊

INTERNATIONAL JOURNAL OF CARDIOLOGY
卷 352, 期 -, 页码 1-8

出版社

ELSEVIER IRELAND LTD
DOI: 10.1016/j.ijcard.2022.02.005

关键词

Atherosclerosis imaging; Coronary plaque; Vulnerable plaque; Computational modeling; Progression prediction

资金

  1. National Natural Science Foundation of China [11672001, 11802060, 11972117]
  2. Natural Science Foundation of Jiangsu Province [BK20180352]
  3. Jiangsu Province Science and Technology Agency [BE2016785]
  4. Fundamental Research Funds for the Central Universities
  5. Zhishan Young Scholars Fund [2242021R41123]

向作者/读者索取更多资源

This article reviews the application of image-based computational modeling in quantifying plaque morphology and biomechanical conditions for understanding plaque evolution and rupture process. The progress made in plaque progression and vulnerability prediction studies is discussed. However, inconsistencies and differences in existing literature make direct comparison and analysis challenging.
Atherosclerotic plaque progression and rupture play an important role in cardiovascular disease development and the final drastic events such as heart attack and stroke. Medical imaging and image-based computational modeling methods advanced considerably in recent years to quantify plaque morphology and biomechanical conditions and gain a better understanding of plaque evolution and rupture process. This article first briefly reviewed clinical imaging techniques for coronary thin-cap fibroatheroma (TCFA) plaques used in image-based computational modeling. This was followed by a summary of different types of biomechanical models for coronary plaques. Plaque progression and vulnerability prediction studies based on image-based computational modeling were reviewed and compared. Much progress has been made and a reasonable high prediction accuracy has been achieved. However, there are still some inconsistencies in existing literature on the impact of biomechanical and morphological factors on future plaque behavior, and it is very difficult to perform direct comparison analysis as differences like image modality, biomechanical factors selection, predictive models, and progression/vulnerability measures exist among these studies. Encouraging data and model sharing across the research community would partially resolve these differences, and possibly lead to clearer assertive conclusions. In vivo image-based computational modeling could be used as a powerful tool for quantitative assessment of coronary plaque vulnerability for potential clinical applications.

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