IVUS-Based FSI Models for Human Coronary Plaque Progression Study: Components, Correlation and Predictive Analysis

Liang Wang, Zheyang Wu, Chun Yang, Jie Zheng, Richard Bach, David Muccigrosso, Kristen Billiar, Akiko Maehara, Gary S. Mintz, Dalin Tang

Research output: Contribution to journalArticlepeer-review

13 Scopus citations

Abstract

Atherosclerotic plaque progression is believed to be associated with mechanical stress conditions. Patient follow-up in vivo intravascular ultrasound coronary plaque data were acquired to construct fluid–structure interaction (FSI) models with cyclic bending to obtain flow wall shear stress (WSS), plaque wall stress (PWS) and strain (PWSn) data and investigate correlations between plaque progression measured by wall thickness increase (WTI), cap thickness increase (CTI), lipid depth increase (LDI) and risk factors including wall thickness (WT), WSS, PWS, and PWSn. Quarter average values (n = 178–1016) of morphological and mechanical factors from all slices were obtained for analysis. A predictive method was introduced to assess prediction accuracy of risk factors and identify the optimal predictor(s) for plaque progression. A combination of WT and PWS was identified as the best predictor for plaque progression measured by WTI. Plaque WT had best overall correlation with WTI (r = −0.7363, p < 1E−10), cap thickness (r = 0.4541, p < 1E−10), CTI (r = −0.4217, p < 1E−8), LD (r = 0.4160, p < 1E−10), and LDI (r = −0.4491, p < 1E−10), followed by PWS (with WTI: (r = −0.3208, p < 1E−10); cap thickness: (r = 0.4541, p < 1E−10); CTI: (r = −0.1719, p = 0.0190); LD: (r = −0.2206, p < 1E−10); LDI: r = 0.1775, p < 0.0001). WSS had mixed correlation results.

Original languageEnglish
Pages (from-to)107-121
Number of pages15
JournalAnnals of biomedical engineering
Volume43
Issue number1
DOIs
StatePublished - Jan 2014

Keywords

  • Coronary
  • Fluid–structure interaction
  • IVUS
  • Plaque progression
  • Plaque rupture

Fingerprint

Dive into the research topics of 'IVUS-Based FSI Models for Human Coronary Plaque Progression Study: Components, Correlation and Predictive Analysis'. Together they form a unique fingerprint.

Cite this