4.3 Article

Imaging central veins in brain lesions with 3-T T2*-weighted magnetic resonance imaging differentiates multiple sclerosis from microangiopathic brain lesions

期刊

MULTIPLE SCLEROSIS JOURNAL
卷 22, 期 10, 页码 1289-1296

出版社

SAGE PUBLICATIONS LTD
DOI: 10.1177/1352458515616700

关键词

Multiple sclerosis; diagnosis; magnetic resonance imaging; sensitivity and specificity

资金

  1. UK Multiple Sclerosis Society [919]
  2. Medical Research Council [G0901321]
  3. Medical Research Council [MC_PC_12019, G0901321] Funding Source: researchfish
  4. MRC [G0901321, MC_PC_12019] Funding Source: UKRI

向作者/读者索取更多资源

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.

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