Literature DB >> 9782595

[Imaging and differentiation of atherosclerotic plaque with magnetic resonance tomography].

C Loewe1, G Oberhuber, R Loewe, H Frank.   

Abstract

Magnetic resonance (MR) imaging studies have allowed the imaging of an atheroma, its size, shape, and lipid contents. The aim of our study was to characterize atherosclerotic lesions using a 0.5 T magnet, to delineate plaque components, and to compare MR results with histology. Thirty necropsy specimens of human iliac arteries were studied. Magnetic resonance imaging studies were carried out on a 0.5 Tesla superconducting magnet using a 5 cm surface coil. The position for the coronal MR planes was oriented by an external marker. The matrix size was 256 x 256, 4 NEX, and the FOV was 45 mm. The pulse sequences used included SE 520/29 and SE 2200/28 and 90. Signal intensity (SI) of fibrous plaques increased significantly from 28.3 +/- 3.8 to 49.1 +/- 8.2 (p < or = 0.0001) and decreased at SE 2200/90 to 24.1 +/- 6.8 (p < or = 0.0001). However, lipid plaque components showed no significant change in SI between T1-weighted pulse-sequences (28.2 +/- 5.4) and T2-weighted pulse-sequences (25.5 +/- 5.9). Only at SE 2200/90 SI of lipid plaques decreased significantly (11.8 +/- 3.9, p < or = 0.0001). As compared to histology, MR has shown a high sensitivity and specificity in the detection of fibrous and lipid plaque components. In conclusions, our study demonstrated that MR is highly effective in the characterization of atherosclerotic lesions.

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Year:  1998        PMID: 9782595     DOI: 10.1007/s003920050221

Source DB:  PubMed          Journal:  Z Kardiol        ISSN: 0300-5860


  1 in total

1.  Deep Learning-Based Automated Detection of Arterial Vessel Wall and Plaque on Magnetic Resonance Vessel Wall Images.

Authors:  Wenjing Xu; Xiong Yang; Yikang Li; Guihua Jiang; Sen Jia; Zhenhuan Gong; Yufei Mao; Shuheng Zhang; Yanqun Teng; Jiayu Zhu; Qiang He; Liwen Wan; Dong Liang; Ye Li; Zhanli Hu; Hairong Zheng; Xin Liu; Na Zhang
Journal:  Front Neurosci       Date:  2022-06-01       Impact factor: 5.152

  1 in total

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