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

Todea et al. Cortical and White Matter Lesions Longitudinal changes in CL and WML count and volume were significantly associated with (i) sustained attention, auditory information, processing speed and flexibility (p < 0.01), (ii) verbal memory (p < 0.01); (iii) verbal fluency (p < 0.05); and (iv) hand-motor function (p < 0.05). Discussion : Changes in cortical and white matter focal damage in early MS patients correlate with global neuroaxonal damage and is associated to cognitive performances.

Manually segmenting multiple sclerosis (MS) cortical lesions (CLs) is extremely time consuming, and past studies have shown only moderate inter‐rater reliability. To accelerate this task, we developed a deep‐learning‐based framework (CLAIMS: Cortical Lesion AI‐Based Assessment in Multiple Sclerosis) for the automated detection and classification of MS CLs with 7 T MRI. Two 7 T datasets, acquired at different sites, were considered. The first consisted of 60 scans that include 0.5 mm isotropic MP2RAGE acquired four times (MP2RAGE×4), 0.7 mm MP2RAGE, 0.5 mm T2*‐weighted GRE, and 0.5 mm T2*‐weighted EPI. The second dataset consisted of 20 scans including only 0.75 × 0.75 × 0.9 mm3 MP2RAGE. CLAIMS was first evaluated using sixfold cross‐validation with single and multi‐contrast 0.5 mm MRI input. Second, the performance of the model was tested on 0.7 mm MP2RAGE images after training with either 0.5 mm MP2RAGE×4, 0.7 mm MP2RAGE, or alternating the two. Third, its generalizability was evaluated on the second external dataset and compared with a state‐of‐the‐art technique based on partial volume estimation and topological constraints (MSLAST). CLAIMS trained only with MP2RAGE×4 achieved results comparable to those of the multi‐contrast model, reaching a CL true positive rate of 74% with a false positive rate of 30%. Detection rate was excellent for leukocortical and subpial lesions (83%, and 70%, respectively), whereas it reached 53% for intracortical lesions. The correlation between disability measures and CL count was similar for manual and CLAIMS lesion counts. Applying a domain‐scanner adaptation approach and testing CLAIMS on the second dataset, the performance was superior to MSLAST when considering a minimum lesion volume of 6 μL (lesion‐wise detection rate of 71% versus 48%). The proposed framework outperforms previous state‐of‐the‐art methods for automated CL detection across scanners and protocols. In the future, CLAIMS may be useful to support clinical decisions at 7 T MRI, especially in the field of diagnosis and differential diagnosis of MS patients.

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BACKGROUND AND PURPOSE: Fully automatic quantification methods of spinal cord compartments are needed to study pathologic changes of the spinal cord GM and WM in MS in vivo. We propose a novel method for automatic spinal cord compartment segmentation (SCORE) in patients with MS. MATERIALS AND METHODS:The cervical spinal cords of 24 patients with MS and 24 sex-and age-matched healthy controls were scanned on a 3T MR imaging system, including an averaged magnetization inversion recovery acquisition sequence. Three experienced raters manually segmented the spinal cord GM and WM, anterior and posterior horns, gray commissure, and MS lesions. Subsequently, manual segmentations were used to train neural segmentation networks of spinal cord compartments with multidimensional gated recurrent units in a 3-fold cross-validation fashion. Total intracranial volumes were quantified using FreeSurfer. RESULTS:The intra-and intersession reproducibility of SCORE was high in all spinal cord compartments (eg, mean relative SD of GM and WM: # 3.50% and #1.47%, respectively) and was better than manual segmentations (all P , .001). The accuracy of SCORE compared with manual segmentations was excellent, both in healthy controls and in patients with MS (Dice similarity coefficients of GM and WM: $ 0.84 and $0.92, respectively). Patients with MS had lower total WM areas (P , .05), and total anterior horn areas (P , .01 respectively), as measured with SCORE. CONCLUSIONS:We demonstrate a novel, reliable quantification method for spinal cord tissue segmentation in healthy controls and patients with MS and other neurologic disorders affecting the spinal cord. Patients with MS have reduced areas in specific spinal cord tissue compartments, which may be used as MS biomarkers.