Computer simulation of intravascular ultrasound plaque images  

作　　者：ZHANG Qi WANG Yuanyuan WANG Weiqi MA Jianying QIAN Juying GE Junbo 

机构地区：[1]Department of Electronic Engineering,Fudan University Shanghai,200433 [2]Department of Cardiology,Zhongshan Hospital of Fudan University Shanghai,200032

出　　处：《Chinese Journal of Acoustics》2009年第4期310-321,共12页声学学报（英文版）

基　　金：supported by the National Basic Research Program of China(2006CB705707);the National Natural Science Foundation of China(30570488,30671998);the Shanghai Leading Academic Discipline Project(B112)

摘　　要：A method is proposed to simulate intravascular ultrasound （IVUS） images of athero-sclerotic plaques. To accomplish the simulation of static IVUS plaque images, the ringdown and guidewire artifacts are introduced into the polar image-formation model （PIFM）, and the fibrous, lipid and calcific contents are synthesized in plaques respectively. The simulation of sequential IVUS images can be achieved by utilizing characteristics of the pulsatile artery. The results on static images demonstrate that it outperforms the PIFM method by 56.9% in terms of the correlation coefficient （CC）, and 24.3% in terms of the mutual information （MI）. The results on sequential images demonstrate that it outperforms the PIFM method by 51.0% in terms of CC, and 10.3% in terms of MI.A method is proposed to simulate intravascular ultrasound （IVUS） images of athero-sclerotic plaques. To accomplish the simulation of static IVUS plaque images, the ringdown and guidewire artifacts are introduced into the polar image-formation model （PIFM）, and the fibrous, lipid and calcific contents are synthesized in plaques respectively. The simulation of sequential IVUS images can be achieved by utilizing characteristics of the pulsatile artery. The results on static images demonstrate that it outperforms the PIFM method by 56.9% in terms of the correlation coefficient （CC）, and 24.3% in terms of the mutual information （MI）. The results on sequential images demonstrate that it outperforms the PIFM method by 51.0% in terms of CC, and 10.3% in terms of MI.

分 类 号：TP391.9[自动化与计算机技术—计算机应用技术] TP391.41[自动化与计算机技术—计算机科学与技术]