Semi-automatic detection of increased susceptibility in multiple sclerosis white matter lesions imaged with 1.5T MRI

Abstract Objective The identification of regions of increased susceptibility (RoIS) in multiple sclerosis (MS) white matter lesions (WML) is currently performed by the radiologist’s visual inspection of magnetic resonance imaging (MRI) data acquired with high-field MRI scanners. The aims of this study were: 1) to define and validate a semi-automatic method for detecting RoIS in WML from quantitative susceptibility maps (QSM) and susceptibility-weighted imaging (SWI) acquired with a 1.5 T MRI scanner; 2) to assess the prevalence of WML with RoIS and the susceptibility in those areas; and 3) to test the association between RoIS in WML and clinical outcomes. Methods Thirty-eight MS patients were scanned on a 1.5 T MRI scanner. T2-hyperintense WML were segmented and superimposed on SWI and QSM images. Two intensity thresholds were defined and consecutively applied for identifying RoIS within WML (thrhyper_QSM to identify QSM hyperintensity within WML and thrhypoSWI to identify SWI hypointensity within WML). The sensitivity and specificity were assessed on a subgroup of subjects. The numbers of WML with RoIS and RoIS volume were determined. Differences between phenotypes and correlations with clinical outcome were tested. Results The method showed good sensitivity (95.6%) and specificity (92.1%). On average, 44.7% of the WML showed RoIS, occupying 11.0% of the total lesion volume, with an average susceptibility of 39.4 ± 12.2 ppb. The number of WML with RoIS was negatively correlated with disease duration (r = −0.342, p = 0.035). Conclusion The proposed semi-automatic method proved to be suitable for the detection of RoIS in WML at 1.5 T. This approach may be useful in longitudinal studies aiming to monitor susceptibility in WML.

[1]  Simon Hametner,et al.  Multiple sclerosis deep grey matter: the relation between demyelination, neurodegeneration, inflammation and iron , 2014, Journal of Neurology, Neurosurgery & Psychiatry.

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

[3]  B. Trapp,et al.  Pathological mechanisms in progressive multiple sclerosis , 2015, The Lancet Neurology.

[4]  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.

[5]  Stephen M Smith,et al.  Fast robust automated brain extraction , 2002, Human brain mapping.

[6]  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.

[7]  S. Zhang,et al.  Quantitative Susceptibility Mapping of Time-Dependent Susceptibility Changes in Multiple Sclerosis Lesions , 2019, American Journal of Neuroradiology.

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

[9]  Pascal Spincemaille,et al.  Magnetic susceptibility increases as diamagnetic molecules breakdown: Myelin digestion during multiple sclerosis lesion formation contributes to increase on QSM , 2018, Journal of magnetic resonance imaging : JMRI.

[10]  Francis Lilley,et al.  Fast and robust three-dimensional best path phase unwrapping algorithm. , 2007, Applied optics.

[11]  Robert Zivadinov,et al.  Brain Iron at Quantitative MRI Is Associated with Disability in Multiple Sclerosis. , 2018, Radiology.

[12]  D. Reich,et al.  Potential role of iron in repair of inflammatory demyelinating lesions. , 2019, The Journal of clinical investigation.

[13]  M. Rovaris,et al.  White Matter Tract Injury is Associated with Deep Gray Matter Iron Deposition in Multiple Sclerosis , 2017, Journal of neuroimaging : official journal of the American Society of Neuroimaging.

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

[15]  Wei Li,et al.  Susceptibility‐weighted imaging and quantitative susceptibility mapping in the brain , 2015, Journal of magnetic resonance imaging : JMRI.

[16]  Sanjeev Chawla,et al.  Longitudinal study of multiple sclerosis lesions using ultra-high field (7T) multiparametric MR imaging , 2018, PloS one.

[17]  H. Lassmann,et al.  Iron related changes in MS lesions and their validity to characterize MS lesion types and dynamics with Ultra‐high field magnetic resonance imaging , 2018, Brain pathology.

[18]  E M Haacke,et al.  Iron and Non-Iron-Related Characteristics of Multiple Sclerosis and Neuromyelitis Optica Lesions at 7T MRI , 2016, American Journal of Neuroradiology.

[19]  Yi Wang,et al.  Clinical feasibility of brain quantitative susceptibility mapping. , 2019, Magnetic resonance imaging.

[20]  W Craelius,et al.  Iron deposits surrounding multiple sclerosis plaques. , 1982, Archives of pathology & laboratory medicine.

[21]  Stephen M. Smith,et al.  A global optimisation method for robust affine registration of brain images , 2001, Medical Image Anal..

[22]  Yi Wang,et al.  Quantitative s usceptibility Mapping of Multiple s clerosis lesions at Various ages 1 , 2014 .

[23]  E. Haacke,et al.  Quantitative susceptibility mapping: current status and future directions. , 2015, Magnetic resonance imaging.