Radiographics : a review publication of the Radiological Society of North America, Inc
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Sarcoidosis is a multisystem granulomatous disorder characterized by development of noncaseating granulomas in various organs. Although the etiology of this condition is unclear, environmental and genetic factors may be substantial in its pathogenesis. Clinical features are often nonspecific, and imaging is essential to diagnosis. ⋯ Although sarcoidosis commonly involves the lungs, it can affect virtually any organ in the body. Computed tomography (CT), magnetic resonance imaging, and positron emission tomography/CT are useful in the diagnosis of extrapulmonary sarcoidosis, but imaging features may overlap with those of other conditions. Familiarity with the spectrum of multimodality imaging findings of sarcoidosis can help to suggest the diagnosis and guide appropriate management. ©RSNA, 2018.
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Acute mesenteric ischemia is a rare life-threatening condition that accounts for approximately one in 1000 hospital admissions. The mortality rate is 50%-69% owing to the absence of specific symptoms and laboratory data, which makes early detection of this condition difficult. If the use of contrast material is possible, biphasic contrast material-enhanced multidetector computed tomography (CT) is the first-line imaging test for early diagnosis of the disease and for differentiation from other causes of acute abdomen. ⋯ The causes of AMI include arterial embolism, arterial thrombosis, venous thrombosis, and nonocclusive mesenteric ischemia, among which arterial causes are far more common than venous causes. Recently, endovascular procedures such as thrombolysis, thrombectomy, thrombus fragmentation, and stent placement have been successfully and safely performed when the ischemia is reversible. Online DICOM image stacks are available for this article. ©RSNA, 2018.
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Deep learning is a class of machine learning methods that are gaining success and attracting interest in many domains, including computer vision, speech recognition, natural language processing, and playing games. Deep learning methods produce a mapping from raw inputs to desired outputs (eg, image classes). Unlike traditional machine learning methods, which require hand-engineered feature extraction from inputs, deep learning methods learn these features directly from data. ⋯ Recently, several clinical applications of CNNs have been proposed and studied in radiology for classification, detection, and segmentation tasks. This article reviews the key concepts of deep learning for clinical radiologists, discusses technical requirements, describes emerging applications in clinical radiology, and outlines limitations and future directions in this field. Radiologists should become familiar with the principles and potential applications of deep learning in medical imaging. ©RSNA, 2017.
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In response to the recommendation of the U. S. ⋯ In this article, a collection of 15 LCS-related scenarios are presented that address situations in which the Lung-RADS guidelines are unclear or situations that are not currently addressed in the Lung-RADS guidelines. For these 15 scenarios, the authors of this article provide the reader with recommendations that are based on their collective experiences, with the hope that future versions of Lung-RADS will provide additional guidance, particularly as more data from widespread LCS are collected and analyzed. ©RSNA, 2017.
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The Liver Imaging Reporting and Data System (LI-RADS) is a reporting system created for the standardized interpretation of liver imaging findings in patients who are at risk for hepatocellular carcinoma (HCC). This system was developed with the cooperative and ongoing efforts of an American College of Radiology-supported committee of diagnostic radiologists with expertise in liver imaging and valuable input from hepatobiliary surgeons, hepatologists, hepatopathologists, and interventional radiologists. In this article, the 2017 version of LI-RADS for computed tomography and magnetic resonance imaging is reviewed. Specific topics include the appropriate population for application of LI-RADS; technical recommendations for image optimization, including definitions of dynamic enhancement phases; diagnostic and treatment response categories; definitions of major and ancillary imaging features; criteria for distinguishing definite HCC from a malignancy that might be non-HCC; management options following LI-RADS categorization; and reporting. ©RSNA, 2017.