Selective inversion recovery quantitative magnetization transfer imaging: Toward a 3 T clinical application in multiple sclerosis

Francesca Bagnato, Giulia Franco, Fei Ye, Run Fan, Patricia Commiskey, Seth A. Smith, Junzhong Xu, Richard Dortch

Research output: Contribution to journalArticlepeer-review

Abstract

Background: Assessing the degree of myelin injury in patients with multiple sclerosis (MS) is challenging due to the lack of magnetic resonance imaging (MRI) methods specific to myelin quantity. By measuring distinct tissue parameters from a two-pool model of the magnetization transfer (MT) effect, quantitative magnetization transfer (qMT) may yield these indices. However, due to long scan times, qMT has not been translated clinically. Objectives: We aim to assess the clinical feasibility of a recently optimized selective inversion recovery (SIR) qMT and to test the hypothesis that SIR-qMT-derived metrics are informative of radiological and clinical disease-related changes in MS. Methods: A total of 18 MS patients and 9 age- and sex-matched healthy controls (HCs) underwent a 3.0 Tesla (3 T) brain MRI, including clinical scans and an optimized SIR-qMT protocol. Four subjects were re-scanned at a 2-week interval to determine inter-scan variability. Results: SIR-qMT measures differed between lesional and non-lesional tissue (p < 0.0001) and between normal-appearing white matter (NAWM) of patients with more advanced disability and normal white matter (WM) of HCs (p < 0.05). SIR-qMT measures were associated with lesion volumes, disease duration, and disability scores (p ⩽ 0.002). Conclusion: SIR-qMT at 3 T is clinically feasible and predicts both radiological and clinical disease severity in MS.

Original languageEnglish
Pages (from-to)457-467
JournalMultiple Sclerosis Journal
Volume26
Issue number4
DOIs
Publication statusPublished - Jan 1 2019

Keywords

  • Biomarkers
  • demyelination
  • multiple sclerosis
  • outcome measurement
  • T2 lesions

ASJC Scopus subject areas

  • Neurology
  • Clinical Neurology

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