Quantitative susceptibility mapping in multiple sclerosis.

PURPOSE To apply quantitative susceptibility mapping (QSM) in the basal ganglia of patients with multiple sclerosis (MS) and relate the findings to R2* mapping with regard to the sensitivity for clinical and morphologic measures of disease severity. MATERIALS AND METHODS The local ethics committee approved this study, and all subjects gave written informed consent. Sixty-eight patients (26 with clinically isolated syndrome, 42 with relapsing-remitting MS) and 23 control subjects underwent 3-T magnetic resonance (MR) imaging. Susceptibility and R2* maps were reconstructed from the same three-dimensional multiecho spoiled gradient-echo sequence. Mean susceptibilities and R2* rates were measured in the basal ganglia and were compared between different phenotypes of the disease (clinically isolated syndrome, MS) and the control subjects by using analysis of variance, and regressing analysis was used to identify independent predictors. RESULTS Compared with control subjects, patients with MS and clinically isolated syndrome had increased (more paramagnetic) magnetic susceptibilities in the basal ganglia. R2* mapping proved less sensitive than QSM regarding group differences. The strongest predictor of magnetic susceptibility was age. Susceptibilities were higher with increasing neurologic deficits (r = 0.34, P < .01) and lower with normalized volumes of gray matter (r = -0.35, P < .005) and the cortex (r = -0.35, P < .005). CONCLUSION QSM provides superior sensitivity over R2* mapping in the detection of MS-related tissue changes in the basal ganglia. With QSM but not with R2* mapping, changes were already observed in patients with clinically isolated syndrome, which suggests that QSM can serve as a sensitive measure at the earliest stage of the disease.

[1]  G J Barker,et al.  Magnetization transfer histograms in clinically isolated syndromes suggestive of multiple sclerosis. , 2005, Brain : a journal of neurology.

[2]  Bing Wu,et al.  Quantitative susceptibility mapping of human brain reflects spatial variation in tissue composition , 2011, NeuroImage.

[3]  M. Núñez,et al.  Iron toxicity in neurodegeneration , 2012, BioMetals.

[4]  D. Pennell,et al.  Myocardial T  2* measurements in iron‐overloaded thalassemia: An in vivo study to investigate optimal methods of quantification , 2008, Magnetic resonance in medicine.

[5]  J. Duyn,et al.  Magnetic susceptibility mapping of brain tissue in vivo using MRI phase data , 2009, Magnetic resonance in medicine.

[6]  R. Cusack,et al.  New Robust 3-D Phase Unwrapping Algorithms: Application to Magnetic Field Mapping and Undistorting Echoplanar Images , 2002, NeuroImage.

[7]  Yi Wang,et al.  Morphology enabled dipole inversion (MEDI) from a single‐angle acquisition: Comparison with COSMOS in human brain imaging , 2011, Magnetic resonance in medicine.

[8]  R. Bakshi,et al.  T1‐ and T2‐Based MRI Measures of Diffuse Gray Matter and White Matter Damage in Patients with Multiple Sclerosis , 2007, Journal of neuroimaging : official journal of the American Society of Neuroimaging.

[9]  Torsten Rohlfing,et al.  MRI estimates of brain iron concentration in normal aging using quantitative susceptibility mapping , 2011, NeuroImage.

[10]  J. Ranjeva,et al.  Atrophy mainly affects the limbic system and the deep grey matter at the first stage of multiple sclerosis , 2010, Journal of Neurology, Neurosurgery & Psychiatry.

[11]  M. Dwyer,et al.  Subcortical and Cortical Gray Matter Atrophy in a Large Sample of Patients with Clinically Isolated Syndrome and Early Relapsing-Remitting Multiple Sclerosis , 2012, American Journal of Neuroradiology.

[12]  Yi Wang,et al.  Calculation of susceptibility through multiple orientation sampling (COSMOS): A method for conditioning the inverse problem from measured magnetic field map to susceptibility source image in MRI , 2009, Magnetic resonance in medicine.

[13]  Chunlei Liu,et al.  Whole brain susceptibility mapping using compressed sensing , 2012, Magnetic resonance in medicine.

[14]  B. Drayer,et al.  Reduced signal intensity on MR images of thalamus and putamen in multiple sclerosis: increased iron content? , 1987, AJR. American journal of roentgenology.

[15]  R. Bakshi,et al.  T2 hypointensity in the deep gray matter of patients with multiple sclerosis: a quantitative magnetic resonance imaging study. , 2002, Archives of neurology.

[16]  B. Hallgren,et al.  THE EFFECT OF AGE ON THE NON‐HAEMIN IRON IN THE HUMAN BRAIN , 1958, Journal of neurochemistry.

[17]  J. Kurtzke.  Rating neurologic impairment in multiple sclerosis , 1983, Neurology.

[18]  À. Rovira,et al.  MR brain iron mapping in MS , 2011, Neurology.

[19]  P. Matthews,et al.  Thalamic neurodegeneration in multiple sclerosis , 2002, Annals of neurology.

[20]  E. Haacke,et al.  High‐resolution BOLD venographic imaging: a window into brain function , 2001, NMR in biomedicine.

[21]  Yi Wang,et al.  Morphology enabled dipole inversion for quantitative susceptibility mapping using structural consistency between the magnitude image and the susceptibility map , 2012, NeuroImage.

[22]  S. Ropele,et al.  Quantitative MR imaging of brain iron: a postmortem validation study. , 2010, Radiology.

[23]  Ferdinand Schweser,et al.  Quantitative susceptibility mapping (QSM) as a means to measure brain iron? A post mortem validation study , 2012, NeuroImage.

[24]  A. J. Thompson,et al.  Magnetic resonance studies of abnormalities in the normal appearing white matter and grey matter in multiple sclerosis , 2003, Journal of Neurology.

[25]  Mark W. Woolrich,et al.  Advances in functional and structural MR image analysis and implementation as FSL , 2004, NeuroImage.

[26]  D. Yablonskiy,et al.  Biophysical mechanisms of phase contrast in gradient echo MRI , 2009, Proceedings of the National Academy of Sciences.

[27]  M. Calabrese,et al.  The predictive value of gray matter atrophy in clinically isolated syndromes , 2011, Neurology.

[28]  Christian Langkammer,et al.  Susceptibility induced gray–white matter MRI contrast in the human brain , 2012, NeuroImage.

[29]  S. Lange,et al.  Adjusting for multiple testing--when and how? , 2001, Journal of clinical epidemiology.

[30]  Stephen M. Smith,et al.  A Bayesian model of shape and appearance for subcortical brain segmentation , 2011, NeuroImage.

[31]  Richard Bowtell,et al.  Whole-brain susceptibility mapping at high field: A comparison of multiple- and single-orientation methods , 2010, NeuroImage.

[32]  S. Reingold,et al.  Diagnostic criteria for multiple sclerosis: 2005 revisions to the “McDonald Criteria” , 2005, Annals of neurology.

[33]  Bing Wu,et al.  High-field (9.4T) MRI of brain dysmyelination by quantitative mapping of magnetic susceptibility , 2011, NeuroImage.

[34]  S Ropele,et al.  Quantitative assessment of brain iron by R2* relaxometry in patients with clinically isolated syndrome and relapsing–remitting multiple sclerosis , 2009, Multiple sclerosis.

[35]  G A Johnson,et al.  MRI of brain iron. , 1986, AJR. American journal of roentgenology.

[36]  Ferdinand Schweser,et al.  Quantitative imaging of intrinsic magnetic tissue properties using MRI signal phase: An approach to in vivo brain iron metabolism? , 2011, NeuroImage.

[37]  J. Lowe,et al.  Regional variations in the extent and pattern of grey matter demyelination in multiple sclerosis: a comparison between the cerebral cortex, cerebellar cortex, deep grey matter nuclei and the spinal cord , 2008, Journal of Neurology, Neurosurgery, and Psychiatry.

[38]  S Ropele,et al.  Determinants of brain iron in multiple sclerosis , 2011, Neurology.

[39]  Rohit Bakshi,et al.  Deep gray matter T2 hypointensity is present in patients with clinically isolated syndromes suggestive of multiple sclerosis , 2010, Multiple sclerosis.

[40]  M. Dwyer,et al.  Iron Deposition on SWI-Filtered Phase in the Subcortical Deep Gray Matter of Patients with Clinically Isolated Syndrome May Precede Structure-Specific Atrophy , 2012, American Journal of Neuroradiology.