Atherosclerotic plaque components characterization and macrophage infiltration identification by intravascular ultrasound elastography based on b-mode analysis: validation in vivo
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Intravascular ultrasound elastography (IVUSE) is a promising imaging technique for early investigation of vulnerable plaques. Compared to radiofrequency signal processing, digital B-mode analysis is simple and of higher portability. However, rare studies have been reported validating the latter technique in vivo. In this study, we developed an IVUSE computer software system involving semi-automatic border delineation and block-matching algorithm and validated the system in vivo. Seven minipigs were fed with atherogenic diet for 40 weeks. For each pig, the endothelium of one side of the renal arteries was denuded at the fifth week. With cross-correlation analysis, Lagrangian strain was calculated from two intravascular ultrasound images acquired in situ. Sixty regions of interests were selected from 35 elastograms matched well with the corresponding histological slices. Plaque types within these regions were classified as fibrous, fibro-fatty or fatty on Masson’s trichrome and Oil-red O staining. Macrophage infiltration was also evaluated with immunohistology. Comparison between the mean strain value of the region of interest and the histological results revealed significant differences in strain values among different plaque types and non-diseased artery walls. The extent of macrophage infiltration was found to be correlated positively with strain values. For identification of fibro-fatty and fibrous plaques and macrophage infiltration, the system showed high sensitivity (93, 96 and 92%, respectively) and specificity (89, 76 and 66%, respectively), as revealed by receiver operating characteristic analysis. Our IVUSE system based on B-mode analysis is capable of characterizing fibrous and fibro-fatty plaques and macrophage intensity, thus holds potential for identifying vulnerable plaque.
KeywordsElastography Intravascular ultrasound Vulnerable plaque Digital image processing Atherosclerosis
The authors gratefully acknowledge the technical assistance provided by Drs. Yan-En Zhang and Xian-Quan Cui. This work was supported by grants from the National 973 Basic Research Program of China (No. 2010CB732605), the Natural Science Foundation of China (No. 60402023 and No. 60831003) and National 863 Hi-Tech Research and Development Program of China (No. 2007AA02Z448).
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