Non-Invasive Identification of Vulnerable Atherosclerotic Plaques Using Texture Analysis in Ultrasound Carotid Elastography: An In Vivo Feasibility Study Validated by Magnetic Resonance Imaging.
The aims of this study were to quantify the textural information of strain rate images in ultrasound carotid elastography and evaluate the feasibility of using the textural features in discriminating stable and vulnerable plaques with magnetic resonance imaging as an in vivo reference. Ultrasound radiofrequency data were acquired in 80 carotid plaques from 52 patients, mainly in the longitudinal imaging view, and axial strain rate images were estimated with an ultrasound carotid elastography technique based on an optical flow algorithm. Four textural features of strain rate images-contrast, homogeneity, correlation and angular second moment-were derived based on the gray-level co-occurrence matrix in plaque regions to quantify the deformation distribution pattern. Conventional elastographic indices based on the magnitude of the absolute strain rate, such as the maximum, mean, median, standard deviation and 99th percentile of the axial strain rate, were also obtained for comparison. Composition measurement with magnetic resonance imaging identified 30 plaques as vulnerable and the other 50 as stable. The four textural features, as well as the magnitude of strain rate images, significantly differed between the two groups of plaques. The best performing features for plaque classification were found to be the contrast and 99th percentile of the absolute strain rate, with a comparative area under the receiver operating characteristic curve of 0.81; a slightly higher maximum accuracy of plaque classification can be achieved by the textural feature of contrast (83.8% vs. 81.3%). The results indicate that the use of texture analysis in plaque classification is feasible and that larger local deformations and higher level of complexity in deformation patterns (associated with the elastic or stiffness heterogeneity of plaque tissues) are more likely to occur in vulnerable plaques.
Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China; Center for Biomedical Imaging Research, Tsinghua University, Beijing, China.
Chengwu
C
Huang
Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China; Center for Biomedical Imaging Research, Tsinghua University, Beijing, China.
Qiong
Q
He
Department of Sonography, China Meitan General Hospital, Beijing, China.
Manwei
M
Huang
Clinical Sites Research Program, Philips Research China, Shanghai, China.
Lingyun
L
Huang
Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China; Center for Biomedical Imaging Research, Tsinghua University, Beijing, China.
Xihai
X
Zhao
Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China; Center for Biomedical Imaging Research, Tsinghua University, Beijing, China; Department of Radiology, University of Washington, Seattle, Washington, USA.
Chun
C
Yuan
Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China; Center for Biomedical Imaging Research, Tsinghua University, Beijing, China. Electronic address: luo_jianwen@tsinghua.edu.cn.
Jianwen
J
Luo
D000368:Aged; D002339:Carotid Arteries / Q000000981:diagnostic imaging*; D054459:Elasticity Imaging Techniques / Q000379:methods*; D005240:Feasibility Studies; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging; D008297:Male; D058226:Plaque, Atherosclerotic / Q000000981:diagnostic imaging*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity
Carotid atherosclerotic plaque; Elastography; Magnetic resonance imaging; Plaque classification; Texture analysis; Vulnerability