Atypical Deep Cerebral Vein Thrombosis with Hemorrhagic Venous Infarction in a Patient Positive for COVID-19

International audienceThere is growing evidence that Severe Acute Respiratory Syndrome coronavirus 2 (SARS-CoV-2) has a neurotropic and neuroinvasive potential. In particular, neurologic complications associated with the infection by SARS-CoV-2 include strokes that may result from a dysregulated inflammatory response to the infection. We report an atypical deep cerebral vein thrombosis complicated with hemorrhagic venous infarction in a patient positive for SARS-CoV-2 with no risk factors for thrombosis

Features of Intestinal Disease Associated With COVID-Related Multisystem Inflammatory Syndrome in Children

Multisystem Inflammatory Syndrome in Children (MIS-C) is a recently identified syndrome that appears to be temporally associated with novel coronavirus 2019 infection. MIS-C presents with fever and evidence of systemic inflammation, which can manifest as cardiovascular, pulmonary, neurologic and gastrointestinal system dysfunction. Presenting gastrointestinal symptoms are seen in the majority, including abdominal pain, diarrhea, and vomiting. Any segment of the gastrointestinal tract may be affected, however inflammation in the ileum and colon predominate. Progressive bowel wall thickening can lead to luminal narrowing and obstruction. Most will have resolution of intestinal inflammation with medical therapies, however in rare instances, surgical resection may be required

Features of Intestinal Disease Associated with COVID-Related Multisystem Inflammatory Syndrome in Children.

Multisystem Inflammatory Syndrome in Children (MIS-C) is a recently identified syndrome that appears to be temporally associated with novel coronavirus 2019 infection. MIS-C presents with fever and evidence of systemic inflammation, which can manifest as cardiovascular, pulmonary, neurologic and gastrointestinal system dysfunction. Presenting gastrointestinal symptoms are seen in the majority, including abdominal pain, diarrhea, and vomiting. Any segment of the gastrointestinal tract may be affected, however inflammation in the ileum and colon predominate. Progressive bowel wall thickening can lead to luminal narrowing and obstruction. Most will have resolution of intestinal inflammation with medical therapies, however in rare instances, surgical resection may be required

Improving the Quality of Synthetic FLAIR Images with Deep Learning Using a Conditional Generative Adversarial Network for Pixel-by-Pixel Image Translation.

BACKGROUND AND PURPOSE:Synthetic FLAIR images are of lower quality than conventional FLAIR images. Here, we aimed to improve the synthetic FLAIR image quality using deep learning with pixel-by-pixel translation through conditional generative adversarial network training.MATERIALS AND METHODS:Forty patients with MS were prospectively included and scanned (3T) to acquire synthetic MR imaging and conventional FLAIR images. Synthetic FLAIR images were created with the SyMRI software. Acquired data were divided into 30 training and 10 test datasets. A conditional generative adversarial network was trained to generate improved FLAIR images from raw synthetic MR imaging data using conventional FLAIR images as targets. The peak signal-to-noise ratio, normalized root mean square error, and the Dice index of MS lesion maps were calculated for synthetic and deep learning FLAIR images against conventional FLAIR images, respectively. Lesion conspicuity and the existence of artifacts were visually assessed.RESULTS:The peak signal-to-noise ratio and normalized root mean square error were significantly higher and lower, respectively, in generated-versus-synthetic FLAIR images in aggregate intracranial tissues and all tissue segments (all P < .001). The Dice index of lesion maps and visual lesion conspicuity were comparable between generated and synthetic FLAIR images (P = 1 and .59, respectively). Generated FLAIR images showed fewer granular artifacts (P = .003) and swelling artifacts (in all cases) than synthetic FLAIR images.CONCLUSIONS:Using deep learning, we improved the synthetic FLAIR image quality by generating FLAIR images that have contrast closer to that of conventional FLAIR images and fewer granular and swelling artifacts, while preserving the lesion contrast