期刊
AMERICAN JOURNAL OF NEURORADIOLOGY
卷 39, 期 10, 页码 1806-1813出版社
AMER SOC NEURORADIOLOGY
DOI: 10.3174/ajnr.A5765
关键词
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资金
- National Institutes of Health from the National Institute of Neurological Disorders and Stroke [RO1 NS085211, R01 NS060910, R21 NS093349]
- National Institutes of Health from the National Institute of Mental Health [R01 MH 112847]
- National Institutes of Health from the National Institute of Biomedical Imaging and Bioengineering [R01 EB 017255]
- Intramural Research Program of National Institute of Neurological Disorders and Stroke [Z01 NS003119]
- National Multiple Sclerosis Society [RG-1507-05243, RG-1707-28586]
BACKGROUND AND PURPOSE: The central vein sign is a promising MR imaging diagnostic biomarker for multiple sclerosis. Recent studies have demonstrated that patients with MS have higher proportions of white matter lesions with the central vein sign compared with those with diseases that mimic MS on MR imaging. However, the clinical application of the central vein sign as a biomarker is limited by interrater differences in the adjudication of the central vein sign as well as the time burden required for the determination of the central vein sign for each lesion in a patient's full MR imaging scan. In this study, we present an automated technique for the detection of the central vein sign in white matter lesions. MATERIALS AND METHODS: Using multimodal MR imaging, the proposed method derives a central vein sign probability, pi(ij), for each lesion, as well as a patient-level central vein sign biomarker, psi(i). The method is probabilistic in nature, allows site-specific lesion segmentation methods, and is potentially robust to intersite variability. The proposed algorithm was tested on imaging acquired at the University of Vermont in 16 participants who have MS and 15 participants who do not. RESULTS: By means of the proposed automated technique, participants with MS were found to have significantly higher values of psi than those without MS ((psi) over bar (MS) = 0.55 0.18; (psi) over bar (non-MS) = 0.31 +/- 0.12; P < .001). The algorithm was also found to show strong discriminative ability between patients with and without MS, with an area under the curve of 0.88. CONCLUSIONS: The current study presents the first fully automated method for detecting the central vein sign in white matter lesions and demonstrates promising performance in a sample of patients with and without MS.
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