Using a 32-echo imaging pulse sequence, T2 relaxation decay curves were acquired from five white- and six gray-matter brain structures outlined in 12 normal volunteers. The water contents of white and gray matter were 0.71 (0.01) and 0.83 (0.03) g/ml, respectively. All white-matter structures had significantly higher myelin water percentages (signal percentage with T2 between 10 and 50 ms) than all gray-matter structures. The range in geometric mean T2 of the main peak for both white and gray matter was from 70 to 86 ms. T2 distributions from the posterior internal capsules and splenium of the corpus callosum were significantly wider (width is related to water environment inhomogeneity) than those from any other white- or gray-matter structures. Thus, quantitative measurement and analysis of T2 relaxation reveals differences in brain tissue water environments not discernible on conventional MR images. These differences may make short T2 components reliable markers for normal myelin.

2 causing COVID-19 prompted the need to gather information on clinical outcomes and risk factors associated with morbidity and mortality in patients with multiple sclerosis (MS) and concomitant SARS-CoV-2 infections. OBJECTIVE To examine outcomes and risk factors associated with COVID-19 clinical severity in a large, diverse cohort of North American patients with MS. DESIGN, SETTING, AND PARTICIPANTS This analysis used deidentified, cross-sectional data on patients with MS and SARS-CoV-2 infection reported by health care professionals in North American academic and community practices between April 1, 2020, and December 12, 2020, in the COVID-19 Infections in MS Registry. Health care professionals were asked to report patients after a minimum of 7 days from initial symptom onset and after sufficient time had passed to observe the COVID-19 disease course through resolution of acute illness or death. Data collection began April 1, 2020, and is ongoing. EXPOSURES Laboratory-positive SARS-CoV-2 infection or highly suspected COVID-19. MAIN OUTCOMES AND MEASURES Clinical outcome with 4 levels of increasing severity: not hospitalized, hospitalization only, admission to the intensive care unit and/or required ventilator support, and death. RESULTS Of 1626 patients, most had laboratory-positive SARS-CoV-2 infection (1345 [82.7%]), were female (1202 [74.0%]), and had relapsing-remitting MS (1255 [80.4%]).A total of 996 patients (61.5%) were non-Hispanic White, 337 (20.8%) were Black, and 190 (11.7%) were Hispanic/Latinx. The mean (SD) age was 47.7 (13.2) years, and 797 (49.5%) had 1 or more comorbidity. The overall mortality rate was 3.3% (95% CI, 2.5%-4.3%). Ambulatory disability and older age were each independently associated with increased odds of all clinical severity levels compared with those not hospitalized after adjusting for other risk factors

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We propose a novel segmentation approach based on deep 3D convolutional encoder networks with shortcut connections and apply it to the segmentation of multiple sclerosis (MS) lesions in magnetic resonance images. Our model is a neural network that consists of two interconnected pathways, a convolutional pathway, which learns increasingly more abstract and higher-level image features, and a deconvolutional pathway, which predicts the final segmentation at the voxel level. The joint training of the feature extraction and prediction pathways allows for the automatic learning of features at different scales that are optimized for accuracy for any given combination of image types and segmentation task. In addition, shortcut connections between the two pathways allow high- and low-level features to be integrated, which enables the segmentation of lesions across a wide range of sizes. We have evaluated our method on two publicly available data sets (MICCAI 2008 and ISBI 2015 challenges) with the results showing that our method performs comparably to the top-ranked state-of-the-art methods, even when only relatively small data sets are available for training. In addition, we have compared our method with five freely available and widely used MS lesion segmentation methods (EMS, LST-LPA, LST-LGA, Lesion-TOADS, and SLS) on a large data set from an MS clinical trial. The results show that our method consistently outperforms these other methods across a wide range of lesion sizes.

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