1. Imaging central veins in brain lesions with 3-T T2*-weighted magnetic resonance imaging differentiates multiple sclerosis from microangiopathic brain lesions.
- Author
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Mistry N, Abdel-Fahim R, Samaraweera A, Mougin O, Tallantyre E, Tench C, Jaspan T, Morris P, Morgan PS, and Evangelou N
- Subjects
- Adult, Aged, Case-Control Studies, Diagnosis, Differential, Female, Humans, Male, Middle Aged, Sensitivity and Specificity, Brain diagnostic imaging, Cerebral Small Vessel Diseases diagnostic imaging, Magnetic Resonance Imaging methods, Multiple Sclerosis diagnostic imaging, Veins diagnostic imaging, White Matter diagnostic imaging
- Abstract
Background: White matter lesions are frequently detected using brain magnetic resonance imaging (MRI) performed for various indications. Most are microangiopathic, but demyelination, including multiple sclerosis (MS), is an important cause; conventional MRI cannot always distinguish between these pathologies. The proportion of lesions with a central vein on 7-T T2*-weighted MRI prospectively distinguishes demyelination from microangiopathic lesions., Objective: To test whether 3-T T2*-weighted MRI can differentiate MS from microangiopathic brain lesions., Methods: A total of 40 patients were studied. Initially, a test cohort of 10 patients with MS and 10 patients with microangiopathic white matter lesions underwent 3-T T2*-weighted brain MRI. Anonymised scans were analysed blind to clinical data, and simple diagnostic rules were devised. These rules were applied to a validation cohort of 20 patients (13 with MS and 7 with microangiopathic lesions) by a blinded observer., Results: Within the test cohort, all patients with MS had central veins visible in >45% of brain lesions, while the rest had central veins visible in <45% of lesions. By applying diagnostic rules to the validation cohort, all remaining patients were correctly categorised., Conclusion: 3-T T2*-weighted brain MRI distinguishes perivenous MS lesions from microangiopathic lesions. Clinical application of this technique could supplement existing diagnostic algorithms., (© The Author(s), 2015.)
- Published
- 2016
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