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Local Maximal Stress Hypothesis and Computational Plaque Vulnerability Index for Atherosclerotic Plaque Assessment

Overview of attention for article published in Annals of Biomedical Engineering, December 2005
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Title
Local Maximal Stress Hypothesis and Computational Plaque Vulnerability Index for Atherosclerotic Plaque Assessment
Published in
Annals of Biomedical Engineering, December 2005
DOI 10.1007/s10439-005-8267-1
Pubmed ID
Authors

Dalin Tang, Chun Yang, Jie Zheng, Pamela K. Woodard, Jeffrey E. Saffitz, Joseph D. Petruccelli, Gregorio A. Sicard, Chun Yuan

Abstract

It is believed that atherosclerotic plaque rupture may be related to maximal stress conditions in the plaque. More careful examination of stress distributions in plaques reveals that it may be the local stress/strain behaviors at critical sites such as very thin plaque cap and locations with plaque cap weakness that are more closely related to plaque rupture risk. A "local maximal stress hypothesis" and a stress-based computational plaque vulnerability index (CPVI) are proposed to assess plaque vulnerability. A critical site selection (CSS) method is proposed to identify critical sites in the plaque and critical stress conditions which are be used to determine CPVI values. Our initial results based on 34 2D MRI slices from 14 human coronary plaque samples indicate that CPVI plaque assessment has an 85% agreement rate (91% if the square root of stress values is used) with assessment given by histopathological analysis. Large-scale and long-term patient studies are needed to further validate our findings for more accurate quantitative plaque vulnerability assessment.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 59 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 2 3%
Spain 1 2%
United States 1 2%
Unknown 55 93%

Demographic breakdown

Readers by professional status Count As %
Student > Master 14 24%
Student > Ph. D. Student 13 22%
Researcher 7 12%
Student > Bachelor 3 5%
Professor > Associate Professor 3 5%
Other 10 17%
Unknown 9 15%
Readers by discipline Count As %
Engineering 27 46%
Medicine and Dentistry 7 12%
Mathematics 5 8%
Computer Science 3 5%
Agricultural and Biological Sciences 2 3%
Other 3 5%
Unknown 12 20%