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AJNR Am J Neuroradiol. 2018 Oct;39(10):1806-1813. doi: 10.3174/ajnr.A5765. Epub 2018 Sep 13.

Automated Integration of Multimodal MRI for the Probabilistic Detection of the Central Vein Sign in White Matter Lesions.

Author information

1
From the Department of Biostatistics, Epidemiology, and Informatics (J.D.D., M.L.M., R.T.S.), Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania jdwor@pennmedicine.upenn.edu.
2
Translational Neuroradiology Section (P.S., M.K.S., D.S.R.), National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, Maryland.
3
Departments of Neurological Sciences (A.S.).
4
Center for Neuroscience and Regenerative Medicine (D.L.P.), Henry M. Jackson Foundation, Bethesda, Maryland.
5
Radiology (R.W.), Larner College of Medicine at the University of Vermont, Burlington, Vermont.
6
From the Department of Biostatistics, Epidemiology, and Informatics (J.D.D., M.L.M., R.T.S.), Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania.
7
Mellen Center for Multiple Sclerosis Treatment and Research (D.O.), Cleveland Clinic, Cleveland, Ohio.
8
Department of Neurology (D.S.R.), Johns Hopkins University School of Medicine, Baltimore, Maryland.

Abstract

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.

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