Objectives: To develop a new automated segmentation method of white-matter (WM) and cortical multiple sclerosis (MS) lesions visible on magnetization-prepared 2 inversion-contrast rapid gradient echo (MP2RAGE) images acquired at 7T MRI. Material and Methods:The proposed method (MSLAST: Multiple Sclerosis Lesion Analysis at Seven Tesla) takes as input a single image contrast derived from the MP2RAGE sequence and is based on partial volume estimation and topological constraints. First, MSLAST performs a skullstrip of MP2RAGE images and computes tissue concentration maps for WM, gray-matter (GM) and cerebrospinal-fluid (CSF) using a partial-volume model of tissues within each voxel. Second, MSLAST performs: i) connected-component analysis to GM and CSF concentration maps to classify small isolated components as MS lesions; ii) hole-filling in the WM concentration map to classify areas with low WM concentration surrounded by WM (i.e. MS lesions); and iii) outlier rejection to the WM mask in order to improve the classification of small WM lesions. Third, MSLAST unifies the three maps obtained from i), ii) and iii) processing steps to generate a global lesion mask.

IMPORTANCEThe mechanisms driving neurodegeneration and brain atrophy in relapsing multiple sclerosis (RMS) are not completely understood.OBJECTIVE To determine whether disability progression independent of relapse activity (PIRA) in patients with RMS is associated with accelerated brain tissue loss. DESIGN, SETTING, AND PARTICIPANTSIn this observational, longitudinal cohort study with median (IQR) follow-up of 3.2 years (2.0-4.9), data were acquired from January 2012 to September 2019 in a consortium of tertiary university and nonuniversity referral hospitals. Patients were included if they had regular clinical follow-up and at least 2 brain magnetic resonance imaging (MRI) scans suitable for volumetric analysis. Data were analyzed between January 2020 and March 2021.EXPOSURES According to the clinical evolution during the entire observation, patients were classified as those presenting (1) relapse activity only, (2) PIRA episodes only, (3) mixed activity, or (4) clinical stability. MAIN OUTCOMES AND MEASURESMean difference in annual percentage change (MD-APC) in brain volume/cortical thickness between groups, calculated after propensity score matching. Brain atrophy rates, and their association with the variables of interest, were explored with linear mixed-effect models.RESULTS Included were 1904 brain MRI scans from 516 patients with RMS (67.4% female; mean [SD] age, 41.4 [11.1] years; median [IQR] Expanded Disability Status Scale score, 2.0 [1.5-3.0]). Scans with insufficient quality were excluded (n = 19). Radiological inflammatory activity was associated with increased atrophy rates in several brain compartments, while an increased annualized relapse rate was linked to accelerated deep gray matter (GM) volume loss. When compared with clinically stable patients, patients with PIRA had an increased rate of brain volume loss (MD-APC, −0.36; 95% CI, −0.60 to −0.12; P = .02), mainly driven by GM loss in the cerebral cortex. Patients who were relapsing presented increased whole brain atrophy (MD-APC, −0.18; 95% CI, −0.34 to −0.02; P = .04) with respect to clinically stable patients, with accelerated GM loss in both cerebral cortex and deep GM. No differences in brain atrophy rates were measured between patients with PIRA and those presenting relapse activity. CONCLUSIONS AND RELEVANCEOur study shows that patients with RMS and PIRA exhibit accelerated brain atrophy, especially in the cerebral cortex. These results point to the need to recognize the insidious manifestations of PIRA in clinical practice and to further evaluate treatment strategies for patients with PIRA in clinical trials.

White-matter lesion count and volume estimation are key to the diagnosis and monitoring of multiple sclerosis (MS). Automated MS lesion segmentation methods that have been proposed in the past 20 years reach their limits when applied to patients in early disease stages characterized by low lesion load and small lesions. We propose an algorithm to automatically assess MS lesion load (number and volume) while taking into account the mixing of healthy and lesional tissue in the image voxels due to partial volume effects. The proposed method works on 3D MPRAGE and 3D FLAIR images as obtained from current routine MS clinical protocols. The method was evaluated and compared with manual segmentation on a cohort of 39 early-stage MS patients with low disability, and showed higher Dice similarity coefficients (median DSC = 0.55) and higher detection rate (median DR = 61%) than two widely used methods (median DSC = 0.50, median DR < 45%) for automated MS lesion segmentation. We argue that this is due to the higher performance in segmentation of small lesions, which are inherently prone to partial volume effects.

BackgroundDetecting new and enlarged lesions in multiple sclerosis (MS) patients is needed to determine their disease activity. LeMan‐PV is a software embedded in the scanner reconstruction system of one vendor, which automatically assesses new and enlarged white matter lesions (NELs) in the follow‐up of MS patients; however, multicenter validation studies are lacking.PurposeTo assess the accuracy of LeMan‐PV for the longitudinal detection NEL white‐matter MS lesions in a multicenter clinical setting.Study TypeRetrospective, longitudinal.SubjectsA total of 206 patients with a definitive MS diagnosis and at least two follow‐up MRI studies from five centers participating in the Swiss Multiple Sclerosis Cohort study. Mean age at first follow‐up = 45.2 years (range: 36.9–52.8 years); 70 males.Field Strength/SequenceFluid attenuated inversion recovery (FLAIR) and T1‐weighted magnetization prepared rapid gradient echo (T1‐MPRAGE) sequences at 1.5 T and 3 T.AssessmentThe study included 313 MRI pairs of datasets. Data were analyzed with LeMan‐PV and compared with a manual “reference standard” provided by a neuroradiologist. A second rater (neurologist) performed the same analysis in a subset of MRI pairs to evaluate the rating‐accuracy. The Sensitivity (Se), Specificity (Sp), Accuracy (Acc), F1‐score, lesion‐wise False‐Positive‐Rate (aFPR), and other measures were used to assess LeMan‐PV performance for the detection of NEL at 1.5 T and 3 T. The performance was also evaluated in the subgroup of 123 MRI pairs at 3 T.Statistical TestsIntraclass correlation coefficient (ICC) and Cohen's kappa (CK) were used to evaluate the agreement between readers.ResultsThe interreader agreement was high for detecting new lesions (ICC = 0.97, Pvalue < 10−20, CK = 0.82, P value = 0) and good (ICC = 0.75, P value < 10−12, CK = 0.68, P value = 0) for detecting enlarged lesions. Across all centers, scanner field strengths (1.5 T, 3 T), and for NEL, LeMan‐PV achieved: Acc = 61%, Se = 65%, Sp = 60%, F1‐score = 0.44, aFPR = 1.31. When both follow‐ups were acquired at 3 T, LeMan‐PV accuracy was higher (Acc = 66%, Se = 66%, Sp = 66%, F1‐score = 0.28, aFPR = 3.03).Data ConclusionIn this multicenter study using clinical data settings acquired at 1.5 T and 3 T, and variations in MRI protocols, LeMan‐PV showed similar sensitivity in detecting NEL with respect to other recent 3 T multicentric studies based on neural networks. While LeMan‐PV performance is not optimal, its main advantage is that it provides automated clinical decision support integrated into the radiological‐routine flow.Evidence Level4Technical EfficacyStage 2