European radiology
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To develop and validate a proof-of-concept convolutional neural network (CNN)-based deep learning system (DLS) that classifies common hepatic lesions on multi-phasic MRI. ⋯ • Deep learning demonstrates high performance in the classification of liver lesions on volumetric multi-phasic MRI, showing potential as an eventual decision-support tool for radiologists. • Demonstrating a classification runtime of a few milliseconds per lesion, a deep learning system could be incorporated into the clinical workflow in a time-efficient manner.
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In patients with acute ischemic stroke, we aimed to investigate whether microvascular changes, as indexed by capillary transit time heterogeneity (CTH), contribute to the decline of the chance for favorable outcome over time and whether they are a predictor of an intracranial hemorrhage (ICH). ⋯ • The classification of potentially salvageable tissue and infarct core based on traditional net perfusion parameters (as Tmax or CBF) does not account for the microvascular distribution of blood. • However, the microvascular distribution of blood, as indexed by the capillary transit time heterogeneity (CTH), directly affects the availability of oxygen within the hypoperfused tissue and should therefore be respected in acute ischemic stroke imaging. • In our study, mildly elevated CTH is found to be a positive predictor for a favorable clinical outcome and a negative predictor for the occurrence of an intracranial hemorrhage in patients with acute ischemic stroke and homogenous mismatch who underwent ET.
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To investigate the diagnostic value of clivopalate angle (CPA) for basilar invagination (BI) at magnetic resonance imaging (MRI). ⋯ • Clivopalate angle has a high diagnostic value for basilar invagination. • Clivopalate angle demonstrates high inter-reader agreement than does clivoaxial angle or clivodens angle. • Clivopalate angle provides complementary information to clivoaxial angle and clivodens angle.