Semi-automatic detection of increased susceptibility in multiple sclerosis white matter lesions imaged with 1.5T MRI. (August 2020)

• Record Type: Journal Article
• Authors: Pelizzari, L.; Bergsland, N.; Utriainen, D.; Viotti, S.; Baglio, F.; Mendozzi, L.; Cecconi, P.; Haacke, E.M.; Zamboni, P.; Laganà, M.M.
• Is Part Of: Biomedical signal processing and control. Volume 61(2020)
• Publication Date: 2020-08
• Subjects: MRI Magnetic Resonance Imaging -- maskNAWM_CC manually defined mask of the NAWM in the corpus callosum splenium -- masklesion_hyperSWI mask of SWI-hyperintense regions within the T2-hyperintense lesions -- MS Multiple sclerosis -- NAWM Normal Appearing White Matter -- QSM Quantitative Susceptibility Mapping -- PD Proton Density -- PP Primary Progressive -- RoIS Regions of Increased Susceptibility -- RR Relapsing-Remitting -- SD Standard Deviation -- SP Secondary Progressive -- SWI Susceptibility Weighted Imaging -- TE Echo Time -- thrhyper_QSM threshold to define QSM hyperintensity -- thrhypoSWI threshold to define SWI hypointensity -- TR Repetition Time -- WM White Matter -- WML White Matter Lesions; Susceptibility -- Quantitative susceptibility mapping -- Susceptibility-weighted imaging -- Multiple sclerosis -- 1.5T -- Semi-automatic method; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2020.102043
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: We propose a semi-automatic method to detect regions of increased susceptibility within MS WM lesions on 1.5 T MRI data. The method proved to be sensitive and specific in detecting regions of increased susceptibility within MS WM lesions. The number of WM lesions with regions of increased susceptibility correlated with disease duration in MS. 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.