Quantitative s usceptibility Mapping of Multiple s clerosis lesions at Various ages 1

PURPOSE To assess multiple sclerosis (MS) lesions at various ages by using quantitative susceptibility mapping (QSM) and conventional magnetic resonance (MR) imaging. MATERIALS AND METHODS Retrospectively selected were 32 clinically confirmed MS patients (nine men and 23 women; 39.3 years ± 10.9) who underwent two MR examinations (interval, 0.43 years ± 0.16) with three-dimensional gradient-echo sequence from August 2011 to August 2012. To estimate the ages of MS lesions, MR examinations performed 0.3-10.6 years before study examinations were studied. Hyperintensity on T2-weighted images was used to define MS lesions. QSM images were reconstructed from gradient-echo data. Susceptibility of MS lesions and temporal rates of change were obtained from QSM images. Lesion susceptibilities were analyzed by t test with intracluster correlation adjustment and Bonferroni correction in multiple comparisons. RESULTS MR imaging of 32 patients depicted 598 MS lesions, of which 162 lesions (27.1%) in 23 patients were age measurable and six (1.0%) were only visible at QSM. The susceptibilities relative to normal-appearing white matter (NAWM) were 0.53 ppb ± 3.34 for acute enhanced lesions, 38.43 ppb ± 13.0 (positive; P < .01) for early to intermediately aged nonenhanced lesions, and 4.67 ppb ± 3.18 for chronic nonenhanced lesions. Temporal rates of susceptibility changes relative to cerebrospinal fluid were 12.49 ppb/month ± 3.15 for acute enhanced lesions, 1.27 ppb/month ± 2.31 for early to intermediately aged nonenhanced lesions, and -0.004 ppb/month ± 0 for chronic nonenhanced lesions. CONCLUSION Magnetic susceptibility of MS lesions increased rapidly as it changed from enhanced to nonenhanced, it attained a high susceptibility value relative to NAWM during its initial few years (approximately 4 years), and it gradually dissipated back to susceptibility similar to that of NAWM as it aged, which may provide new insight into pathophysiologic features of MS lesions. Online supplemental material is available for this article.

[1]  C. W. Adams,et al.  Perivascular iron deposition and other vascular damage in multiple sclerosis. , 1988, Journal of neurology, neurosurgery, and psychiatry.

[2]  R. Rudick,et al.  Axonal transection in the lesions of multiple sclerosis. , 1998, The New England journal of medicine.

[3]  K S Panageas,et al.  Statistical issues in analysis of diagnostic imaging experiments with multiple observations per patient. , 2001, Radiology.

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

[5]  F. Barkhof.  The clinico‐radiological paradox in multiple sclerosis revisited , 2002, Current opinion in neurology.

[6]  S. Nelson,et al.  Quantitative in vivo magnetic resonance imaging of multiple sclerosis at 7 Tesla with sensitivity to iron , 2008, Annals of neurology.

[7]  R Marc Lebel,et al.  Detecting lesions in multiple sclerosis at 4.7 tesla using phase susceptibility‐weighting and T2‐weighting , 2009, Journal of magnetic resonance imaging : JMRI.

[8]  R. Grossman,et al.  Characterizing iron deposition in multiple sclerosis lesions using susceptibility weighted imaging , 2009, Journal of magnetic resonance imaging : JMRI.

[9]  Jeff H. Duyn,et al.  Susceptibility contrast in high field MRI of human brain as a function of tissue iron content , 2009, NeuroImage.

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

[11]  L. Kappos,et al.  Black holes in multiple sclerosis: definition, evolution, and clinical correlations , 2009, Acta neurologica Scandinavica.

[12]  M. Fukunaga,et al.  Sensitivity of MRI resonance frequency to the orientation of brain tissue microstructure , 2010, Proceedings of the National Academy of Sciences.

[13]  K. Trinkaus,et al.  Increased diffusivity in acute multiple sclerosis lesions predicts risk of black hole , 2010, Neurology.

[14]  Yi Wang,et al.  Quantitative susceptibility map reconstruction from MR phase data using bayesian regularization: Validation and application to brain imaging , 2010, Magnetic resonance in medicine.

[15]  Hellmut Merkle,et al.  Tracking iron in multiple sclerosis: a combined imaging and histopathological study at 7 Tesla. , 2011, Brain : a journal of neurology.

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

[17]  Christian Langkammer,et al.  Iron and Neurodegeneration in Multiple Sclerosis , 2011, Multiple sclerosis international.

[18]  Massimo Filippi,et al.  7. Mri Assessment of Iron Deposition in Multiple Sclerosis , 2022 .

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

[20]  Robert Zivadinov,et al.  Recent Developments in Imaging of Multiple Sclerosis , 2011, The neurologist.

[21]  Siegfried Trattnig,et al.  Analysis of multiple sclerosis lesions using a fusion of 3.0 T FLAIR and 7.0 T SWI phase: FLAIR SWI , 2011, Journal of magnetic resonance imaging : JMRI.

[22]  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.

[23]  Steven M LeVine,et al.  Pathogenic implications of iron accumulation in multiple sclerosis , 2012, Journal of neurochemistry.

[24]  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.

[25]  Robert Zivadinov,et al.  Iron deposition in multiple sclerosis lesions measured by susceptibility‐weighted imaging filtered phase: A case control study , 2012, Journal of magnetic resonance imaging : JMRI.

[26]  Weiyu Xu,et al.  Accuracy of the Morphology Enabled Dipole Inversion (MEDI) Algorithm for Quantitative Susceptibility Mapping in MRI , 2012, IEEE Transactions on Medical Imaging.

[27]  J. Reichenbach,et al.  Assessing Abnormal Iron Content in the Deep Gray Matter of Patients with Multiple Sclerosis versus Healthy Controls , 2012, American Journal of Neuroradiology.

[28]  R. Bowtell,et al.  Fiber orientation-dependent white matter contrast in gradient echo MRI , 2012, Proceedings of the National Academy of Sciences.

[29]  Min Zhang,et al.  Reducing the object orientation dependence of susceptibility effects in gradient echo MRI through quantitative susceptibility mapping , 2012, Magnetic resonance in medicine.

[30]  N. Richert,et al.  Characterizing contrast-enhancing and re-enhancing lesions in multiple sclerosis , 2012, Neurology.

[31]  Hellmut Merkle,et al.  Chronic multiple sclerosis lesions: characterization with high-field-strength MR imaging. , 2012, Radiology.

[32]  Dmitriy A Yablonskiy,et al.  Biophysical mechanisms of MRI signal frequency contrast in multiple sclerosis , 2012, Proceedings of the National Academy of Sciences.

[33]  Pascal Spincemaille,et al.  Cerebral microbleeds: burden assessment by using quantitative susceptibility mapping. , 2012, Radiology.

[34]  David Pitt,et al.  Iron Is a Sensitive Biomarker for Inflammation in Multiple Sclerosis Lesions , 2013, PloS one.

[35]  Margit Jehna,et al.  Quantitative susceptibility mapping in multiple sclerosis. , 2013, Radiology.

[36]  Cris S Constantinescu,et al.  Increased iron accumulation occurs in the earliest stages of demyelinating disease: an ultra-high field susceptibility mapping study in Clinically Isolated Syndrome , 2013, Multiple sclerosis.

[37]  R Marc Lebel,et al.  Multiple sclerosis: validation of MR imaging for quantification and detection of iron. , 2013, Radiology.

[38]  Pascal Spincemaille,et al.  Nonlinear formulation of the magnetic field to source relationship for robust quantitative susceptibility mapping , 2013, Magnetic resonance in medicine.

[39]  Duan Xu,et al.  A serial in vivo 7T magnetic resonance phase imaging study of white matter lesions in multiple sclerosis , 2013, Multiple sclerosis.