Sensitivity of the Inhomogeneous Magnetization Transfer Imaging Technique to Spinal Cord Damage in Multiple Sclerosis

Anatomic images covering the cervical spinal cord from the C1 to C6 levels and DTI, magnetization transfer/inhomogeneous magnetization transfer images at the C2/C5 levels were acquired in 19 patients with MS and 19 paired healthy controls. Anatomic images were segmented in spinal cord GM and WM, both manually and using the AMU40 atlases. MS lesions were manually delineated. MR imaging metrics were analyzed within normal-appearing and lesion regions in anterolateral and posterolateral WM and compared using Wilcoxon rank tests and z scores. The use of a multiparametric MR imaging protocol combined with an automatic template-based GM/WM segmentation approach in the current study outlined a higher sensitivity of the ihMT technique toward spinal cord pathophysiologic changes in MS compared with atrophy measurements, DTI, and conventional MT. The authors also conclude that the clinical correlations between ihMTR and functional impairment observed in patients with MS also argue for its potential clinical relevance, paving the way for future longitudinal multicentric clinical trials in MS. BACKGROUND AND PURPOSE: The inhomogeneous magnetization transfer technique has demonstrated high specificity for myelin, and has shown sensitivity to multiple sclerosis-related impairment in brain tissue. Our aim was to investigate its sensitivity to spinal cord impairment in MS relative to more established MR imaging techniques (volumetry, magnetization transfer, DTI). MATERIALS AND METHODS: Anatomic images covering the cervical spinal cord from the C1 to C6 levels and DTI, magnetization transfer/inhomogeneous magnetization transfer images at the C2/C5 levels were acquired in 19 patients with MS and 19 paired healthy controls. Anatomic images were segmented in spinal cord GM and WM, both manually and using the AMU40 atlases. MS lesions were manually delineated. MR metrics were analyzed within normal-appearing and lesion regions in anterolateral and posterolateral WM and compared using Wilcoxon rank tests and z scores. Correlations between MR metrics and clinical scores in patients with MS were evaluated using the Spearman rank correlation. RESULTS: AMU40-based C1-to-C6 GM/WM automatic segmentations in patients with MS were evaluated relative to manual delineation. Mean Dice coefficients were 0.75/0.89, respectively. All MR metrics (WM/GM cross-sectional areas, normal-appearing and lesion diffusivities, and magnetization transfer/inhomogeneous magnetization transfer ratios) were observed altered in patients compared with controls (P < .05). Additionally, the absolute inhomogeneous magnetization transfer ratio z scores were significantly higher than those of the other MR metrics (P < .0001), suggesting a higher inhomogeneous magnetization transfer sensitivity toward spinal cord impairment in MS. Significant correlations with the Expanded Disability Status Scale (ρ = –0.73/P = .02, ρ = –0.81/P = .004) and the total Medical Research Council scale (ρ = 0.80/P = .009, ρ = –0.74/P = .02) were observed for inhomogeneous magnetization transfer and magnetization transfer ratio z scores, respectively, in normal-appearing WM regions, while weaker and nonsignificant correlations were obtained for DTI metrics. CONCLUSIONS: With inhomogeneous magnetization transfer being highly sensitive to spinal cord damage in MS compared with conventional magnetization transfer and DTI, it could generate great clinical interest for longitudinal follow-up and potential remyelinating clinical trials. In line with other advanced myelin techniques with which it could be compared, it opens perspectives for multicentric investigations.

[1]  April AIDS TO THE INVESTIGATION OF PERIPHERAL NERVE INJURIES , 1943 .

[2]  R. Balaban,et al.  Magnetization transfer contrast (MTC) and tissue water proton relaxation in vivo , 1989, Magnetic resonance in medicine.

[3]  P. Basser,et al.  MR diffusion tensor spectroscopy and imaging. , 1994, Biophysical journal.

[4]  A. MacKay,et al.  In vivo visualization of myelin water in brain by magnetic resonance , 1994, Magnetic resonance in medicine.

[5]  Stephen M. Rao,et al.  Development of a multiple sclerosis functional composite as a clinical trial outcome measure. , 1999, Brain : a journal of neurology.

[6]  G. B. Pike,et al.  Quantitative imaging of magnetization transfer exchange and relaxation properties in vivo using MRI , 2001, Magnetic resonance in medicine.

[7]  Michael Brady,et al.  Improved Optimization for the Robust and Accurate Linear Registration and Motion Correction of Brain Images , 2002, NeuroImage.

[8]  Stephen M. Smith,et al.  Improved Optimization for the Robust and Accurate Linear Registration and Motion Correction of Brain Images , 2002, NeuroImage.

[9]  M. Filippi Magnetization transfer MRI in multiple sclerosis and other central nervous system disorders , 2003, European journal of neurology.

[10]  R. Ordidge,et al.  High field MRI correlates of myelin content and axonal density in multiple sclerosis , 2003, Journal of Neurology.

[11]  M. Filippi,et al.  Magnetization Transfer MRI in Multiple Sclerosis , 2007, Journal of neuroimaging : official journal of the American Society of Neuroimaging.

[12]  David H. Miller,et al.  Quantitative magnetization transfer imaging in postmortem multiple sclerosis brain , 2007, Journal of magnetic resonance imaging : JMRI.

[13]  Eliza M. Gordon-Lipkin,et al.  Sensorimotor dysfunction in multiple sclerosis and column-specific magnetization transfer-imaging abnormalities in the spinal cord. , 2009, Brain : a journal of neurology.

[14]  1995 Liu,et al.  United States Patent , 2011 .

[15]  Jeffrey A. Cohen,et al.  Diagnostic criteria for multiple sclerosis: 2010 Revisions to the McDonald criteria , 2011, Annals of neurology.

[16]  Mark W. Woolrich,et al.  FSL , 2012, NeuroImage.

[17]  Jerry L Prince,et al.  Multiparametric MRI correlates of sensorimotor function in the spinal cord in multiple sclerosis , 2013, Multiple sclerosis.

[18]  Julien Cohen-Adad,et al.  The current state-of-the-art of spinal cord imaging: Methods , 2014, NeuroImage.

[19]  O. Ciccarelli,et al.  Spinal cord grey matter abnormalities are associated with secondary progression and physical disability in multiple sclerosis , 2014, Journal of Neurology, Neurosurgery & Psychiatry.

[20]  Richard D. Dortch,et al.  Rapid, high-resolution quantitative magnetization transfer MRI of the human spinal cord , 2014, NeuroImage.

[21]  D. L. Collins,et al.  Framework for integrated MRI average of the spinal cord white and gray matter: The MNI–Poly–AMU template , 2014, NeuroImage.

[22]  Julien Cohen-Adad,et al.  Robust, accurate and fast automatic segmentation of the spinal cord , 2014, NeuroImage.

[23]  Kesshi M Jordan,et al.  Spinal cord gray matter atrophy correlates with multiple sclerosis disability , 2014, Annals of neurology.

[24]  D. Alsop,et al.  Magnetization transfer from inhomogeneously broadened lines: A potential marker for myelin , 2015, Magnetic Resonance in Medicine.

[25]  Julien Cohen-Adad,et al.  White matter atlas of the human spinal cord with estimation of partial volume effect , 2015, NeuroImage.

[26]  Julien Cohen-Adad,et al.  A reliable spatially normalized template of the human spinal cord — Applications to automated white matter/gray matter segmentation and tensor-based morphometry (TBM) mapping of gray matter alterations occurring with age , 2015, NeuroImage.

[27]  O. Ciccarelli,et al.  MRI monitoring of pathological changes in the spinal cord in patients with multiple sclerosis , 2015, The Lancet Neurology.

[28]  G. B. Pike,et al.  MRI‐based myelin water imaging: A technical review , 2015, Magnetic resonance in medicine.

[29]  David H. Miller,et al.  Fully automated grey and white matter spinal cord segmentation , 2016, Scientific Reports.

[30]  J. Gore,et al.  Magnetic resonance imaging of the cervical spinal cord in multiple sclerosis at 7T , 2016, Multiple sclerosis.

[31]  J. Ranjeva,et al.  Tract‐specific and age‐related variations of the spinal cord microstructure: a multi‐parametric MRI study using diffusion tensor imaging (DTI) and inhomogeneous magnetization transfer (ihMT) , 2016, NMR in biomedicine.

[32]  Aurélien Massire,et al.  High-resolution multi-parametric quantitative magnetic resonance imaging of the human cervical spinal cord at 7T , 2016, NeuroImage.

[33]  Julien Cohen-Adad,et al.  SCT: Spinal Cord Toolbox, an open-source software for processing spinal cord MRI data , 2017, NeuroImage.

[34]  J. Hermsdörfer,et al.  Predictive and Reactive Grip Force Responses to Rapid Load Increases in People With Multiple Sclerosis. , 2017, Archives of physical medicine and rehabilitation.

[35]  J. Ranjeva,et al.  Region‐specific impairment of the cervical spinal cord (SC) in amyotrophic lateral sclerosis: A preliminary study using SC templates and quantitative MRI (diffusion tensor imaging/inhomogeneous magnetization transfer) , 2017, NMR in biomedicine.

[36]  Manuel Taso,et al.  Magnetization transfer from inhomogeneously broadened lines (ihMT): Improved imaging strategy for spinal cord applications , 2017, Magnetic resonance in medicine.

[37]  Alyssa H. Zhu,et al.  Gray matter segmentation of the spinal cord with active contours in MR images , 2017, NeuroImage.

[38]  D. Alsop,et al.  In vivo measurement of a new source of contrast, the dipolar relaxation time, T1D, using a modified inhomogeneous magnetization transfer (ihMT) sequence , 2017, Magnetic resonance in medicine.

[39]  J. Sastre-Garriga Clinical monitoring of multiple sclerosis should routinely include spinal cord imaging – Commentary , 2018, Multiple sclerosis.

[40]  David H. Miller,et al.  Diagnosis of multiple sclerosis: 2017 revisions of the McDonald criteria , 2017, The Lancet Neurology.

[41]  Jerry L Prince,et al.  Longitudinal Changes in Quantitative Spinal Cord MRI in Multiple Sclerosis Patients: Results of a 5-year Study (S47.001) , 2018 .

[42]  M. Filippi,et al.  Cervical Cord T1-weighted Hypointense Lesions at MR Imaging in Multiple Sclerosis: Relationship to Cord Atrophy and Disability. , 2018, Radiology.

[43]  K. Kantarci,et al.  Cervical spinal cord atrophy , 2018, Neurology: Neuroimmunology & Neuroinflammation.

[44]  D. Alsop,et al.  Evaluation of the Sensitivity of Inhomogeneous Magnetization Transfer (ihMT) MRI for Multiple Sclerosis , 2018, American Journal of Neuroradiology.

[45]  D. Alsop,et al.  Whole brain inhomogeneous magnetization transfer (ihMT) imaging: Sensitivity enhancement within a steady‐state gradient echo sequence , 2018, Magnetic resonance in medicine.

[46]  H. Johansen-Berg,et al.  Advances in noninvasive myelin imaging , 2017, Developmental neurobiology.

[47]  Julien Cohen-Adad,et al.  PAM50: Unbiased multimodal template of the brainstem and spinal cord aligned with the ICBM152 space , 2018, NeuroImage.

[48]  Julien Cohen-Adad,et al.  Automatic segmentation of the spinal cord and intramedullary multiple sclerosis lesions with convolutional neural networks , 2018, NeuroImage.

[49]  Ninon Burgos,et al.  New advances in the Clinica software platform for clinical neuroimaging studies , 2019 .

[50]  O. Ciccarelli,et al.  Spatial distribution of multiple sclerosis lesions in the cervical spinal cord , 2019, Brain : a journal of neurology.

[51]  I. Vavasour,et al.  Quantitative neuroimaging measures of myelin in the healthy brain and in multiple sclerosis , 2019, Human brain mapping.

[52]  Adam V. Dvorak,et al.  Rapid myelin water imaging for the assessment of cervical spinal cord myelin damage , 2019, NeuroImage: Clinical.