Francesca Bagnato1, Giulia Franco2, Fei Ye3, Run Fan3, Patricia Commiskey4, Seth A Smith5, Junzhong Xu6, Richard Dortch6. 1. Neuroimaging Unit, Division of Neuroimmunology, Department of Neurology, Vanderbilt University Medical Center (VUMC), Nashville, TN, USA. 2. Neuroimaging Unit, Division of Neuroimmunology, Department of Neurology, Vanderbilt University Medical Center (VUMC), Nashville, TN, USA/ IRCCS Foundation Ca' Granda Ospedale Maggiore Policlinico, Dino Ferrari Center, Neuroscience Section, Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy. 3. Department of Biostatistics, Vanderbilt University Medical Center (VUMC), Nashville, TN, USA. 4. Department of Neurology, Stroke Division, Vanderbilt University Medical Center (VUMC), Nashville, TN, USA. 5. Vanderbilt University Institute of Imaging Science, Department of Radiology and Radiological Sciences and Department of Ophthalmology, Vanderbilt University Medical Center (VUMC), Nashville, TN, USA/ Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA. 6. Vanderbilt University Institute of Imaging Science, Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center (VUMC), Nashville, TN, USA/ Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA.
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.
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 MSpatients 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.
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