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, πij, for each lesion, as well as a 
patient-level central vein sign biomarker, ψi. 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 ψ than those without MS (ψMS = 0.55 
± 0.18; ψnon-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.