Contributing Editor Greg Freiherr offers an overview of computed tomography (CT) advances at the Radiological Society of North America (RSNA) 2015. The video includes Freiherr during his booth tours with some of the key vendors who were featuring new technology.
Computed Tomography (CT)
Artificial intelligence (AI)-assisted software was used to identify inflammatory tissues in lung and automatically segment inflammatory lesions. Three-dimensional image shows regions of COVID-19 pneumonia in lung through AI postprocessing. Image courtesy of the American Journal of Roentgenology (AJR)
Thoracic findings in a 15-year-old girl with Multisystem Inflammatory Syndrome in Children (MIS-C). (a) Chest radiograph on admission shows mild perihilar bronchial wall cuffing. (b) Chest radiograph on the third day of admission demonstrates extensive airspace opacification with a mid and lower zone predominance. (c, d) Contrast-enhanced axial CT chest of the thorax at day 3 shows areas of ground-glass opacification (GGO) and dense airspace consolidation with air bronchograms. (c) This conformed to a mosaic pattern with a bronchocentric distribution to the GGO (white arrow, d) involving both central and peripheral lung parenchyma with pleural effusions (black small arrow, d). image courtesy of Radiological Society of North America
For procedural delays that will not adversely affect patient outcome, Fananapazir and colleagues proposed the following tiered approach for both outpatient and inpatient scenarios: urgent procedures, procedures that should be performed within 2 weeks, procedures that should be performed within 2 months, and procedures that can safely be delayed 2 or 6 months. Courtesy of American Journal of Roentgenology (AJR)
Figure 1: Examples of chest CT images of COVID-19 (+) patients and visualization of features correlated to COVID-19 positivity. For each pair of images, the left image is a CT image showing the segmented lung used as input for the CNN (convolutional neural network algorithm) model trained on CT images only, and the right image shows the heatmap of pixels that the CNN model classified as having SARS-CoV-2 infection (red indicates higher probability). (a) A 51-year-old female with fever and history of exposure to SARS-CoV-2. The CNN model identified abnormal features in the right lower lobe (white color), whereas the two radiologists labeled this CT as negative. (b) A 52-year-old female who had a history of exposure to SARS-CoV-2 and presented with fever and productive cough. Bilateral peripheral ground-glass opacities (arrows) were labeled by the radiologists, and the CNN model predicted positivity based on features in matching areas. (c) A 72-year-old female with exposure history to the animal market in Wuhan presented with fever and productive cough. The segmented CT image shows ground-glass opacity in the anterior aspect of the right lung (arrow), whereas the CNN model labeled this CT as negative. (d) A 59-year-old female with cough and exposure history. The segmented CT image shows no evidence of pneumonia, and the CNN model also labeled this CT as negative.
Axial (A) and coronal (B) CT of the abdomen and pelvis with IV contrast in a 57-year-old man with a high clinical suspicion for bowel ischemia. There was generalized small bowel distension and segmental thickening (arrows), with adjacent mesenteric congestion (thin arrow in B), and a small volume of ascites (* in B). Findings are nonspecific but suggestive of early ischemia or infection. Image courtesy of RSNA