title: Identification of early atherosclerotic lesions in carotid arteries with quantitative characteristics measured by 3D MRI.
abstract: To evaluate the usefulness of quantitative characteristics of morphology and signal intensity of arterial wall measured by 3D multicontrast magnetic resonance vessel wall imaging (MRVWI) in identification of carotid early atherosclerosis (CEAS).
  
  
  
  In all, 61 older subjects (mean age 71.8 ± 5.6 years old; 25 males) without cardiovascular symptoms in the last 6 months were recruited. The carotid arteries without advanced plaque features on 3.0T MRI were included for analysis. Ultrasound imaging was used as a reference to identify CEAS. The morphological parameters including lumen area (LA), wall area (WA), wall thickness (WT), and normalized wall index (NWI = WA/[WA+LA] × 100%) and the signal intensity on 3.0T MR T2-weighted images (T2SI) of the carotid arterial wall were measured. Three regression models were built to identify CEAS with the following parameters: Model 1 with both morphological and T2SI parameters; Model 2 with T2SI parameters; and Model 3 with morphological parameters. All models were adjusted for age and sex. Area under the curve (AUC) was calculated to validate models.
  
  
  
  Of the 86 carotid arteries without advanced plaques, 47 (54.7%) were found to have early plaques determined by ultrasound. Among three regression models, Model 1 showed the highest AUC values in identifying CEAS (left: AUC = 0.856, P < 0.001; right: AUC = 0.867, P < 0.001), followed by Model 2 (left: AUC = 0.843, P < 0.001; right: AUC = 0.798, P = 0.001), and Model 3 (left: AUC = 0.790, P = 0.002; right: AUC = 0.806, P < 0.001).
  
  
  
  The combination of morphology and normalized T2SI of arterial wall measured by MRVWI is more effective than each characteristic alone in identification of CEAS. J. Magn. Reson. Imaging 2016;44:1270-1276.
authors.affiliation: Center for Biomedical Imaging Research, Department of Biomedical Engineering, Tsinghua University School of Medicine, Beijing, China.
authors.forename: Huiyu
authors.initials: H
authors.lastname: Qiao
authors.affiliation: Center for Biomedical Imaging Research, Department of Biomedical Engineering, Tsinghua University School of Medicine, Beijing, China.
authors.forename: Qiong
authors.initials: Q
authors.lastname: He
authors.affiliation: Center for Biomedical Imaging Research, Department of Biomedical Engineering, Tsinghua University School of Medicine, Beijing, China.
authors.forename: Zhensen
authors.initials: Z
authors.lastname: Chen
authors.affiliation: Department of Radiology, University of Washington, Seattle, Washington, USA.
authors.forename: Dongxiang
authors.initials: D
authors.lastname: Xu
authors.affiliation: Clinical Sites Research Program, Philips Research China, Shanghai, China.
authors.forename: Lingyun
authors.initials: L
authors.lastname: Huang
authors.affiliation: Center for Biomedical Imaging Research, Department of Biomedical Engineering, Tsinghua University School of Medicine, Beijing, China.
authors.forename: Le
authors.initials: L
authors.lastname: He
authors.affiliation: Philips Healthcare (Suzhou), Jiangsu, China.
authors.forename: Li
authors.initials: L
authors.lastname: Jiang
authors.affiliation: Center for Biomedical Imaging Research, Department of Biomedical Engineering, Tsinghua University School of Medicine, Beijing, China.
authors.forename: Rui
authors.initials: R
authors.lastname: Li
authors.affiliation: Center for Biomedical Imaging Research, Department of Biomedical Engineering, Tsinghua University School of Medicine, Beijing, China.
authors.forename: Jianwen
authors.initials: J
authors.lastname: Luo
authors.affiliation: Center for Biomedical Imaging Research, Department of Biomedical Engineering, Tsinghua University School of Medicine, Beijing, China.|Department of Radiology, University of Washington, Seattle, Washington, USA.
authors.forename: Chun
authors.initials: C
authors.lastname: Yuan
authors.affiliation: Center for Biomedical Imaging Research, Department of Biomedical Engineering, Tsinghua University School of Medicine, Beijing, China. xihaizhao@tsinghua.edu.cn.
authors.forename: Xihai
authors.initials: X
authors.lastname: Zhao
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D002340:Carotid Artery Diseases / Q000000981:diagnostic imaging* / Q000473:pathology*; D042241:Early Diagnosis; D005260:Female; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D008875:Middle Aged; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity
keywords: T2 signal intensity; carotid artery; early atherosclerosis; magnetic resonance imaging; morphology