Computed Tomography (CT)
Computed tomography (CT) systems use a series of X-ray images to create an image volume dataset with slices that can be manipulated on any plane using advanced visualization software. The section includes computed tomography scanners, CT contrast agents, CT angiography (CTA and CCTA), CT perfusion, spectral CT (dual-source CT), and iterative reconstruction dose reduction software.
A, Image from noncontrast head CT demonstrates symmetric hypoattenuation within the bilateral medial thalami (arrows). B, Axial CT venogram demonstrates patency of the cerebral venous vasculature, including the internal cerebral veins (arrows). C, Coronal reformat of aCT angiogram demonstrates normal appearance of the basilar artery and proximal posterior cerebral arteries. Image courtesy of the Radiological Society of North America (RSNA)
Typical CT imaging features for COVID-19. Unenhanced, thin-section axial images of the lungs in a 52-year-old man with a positive RT-PCR (A-D) show bilateral, multifocal rounded (asterisks) and peripheral GGO (arrows) with superimposed interlobular septal thickening and visible intralobular lines (“crazy-paving”). Routine screening CT for diagnosis or exclusion of COVID-19 is currently not recommended by most professional organizations or the US Centers for Disease Control and Prevention. Image courtesy of RSNA
Series CT scans in 35-year-old woman with COVID-19 pneumonia. (a) Scan obtained on illness days 1 showed multiple pure ground-glass opacity (GGO) mainly in right lower lobe. (b) Scan obtained on illness days 5 showed increased extent of GGO and early consolidation. (c) Scan obtained on illness days 11 showed multiple consolidation with almost the same extent. (d) Scan obtained on illness days 15 showed a mixed pattern with a slightly smaller extent, and the perilobular consolidation might suggest the presence of organizing pneumonia. The patient was discharged on illness days 17. Image courtesy of the journal Radiology
Two examples of CT myocardial perfusion (CTP) imaging assessment software. Canon is on the left and GE Healthcare is on the right. Both of these technologies have been around for a few years, but there have been an increasing amount of clinical data from studies showing the accuracy of the technology compared to nuclear imaging, the current stand of care for myocardial perfusion imaging, and cardiac MRI.
Figure 1: Depiction of the fully automated CT biomarkers tools used in this study. (A) Schematic depiction of the automated process for assessing fat, muscle, liver, aortic calcification, and bone from original abdominal CT scan data. (B) Case example in an asymptomatic 52-year-old man undergoing CT for colorectal cancer screening. At the time of CT screening, he had a body-mass index of 27·3 and Framingham risk score of 5% (low risk). However, several CT-based metabolic markers were indicative of underlying disease. Multivariate Cox model prediction based on these three CT-based results put the risk of cardiovascular event at 19% within 2 years, at 40% within 5 years, and at 67% within 10 years, and the risk of death at 4% within 2 years, 11% within 5 years, and 27% within 10 years. At longitudinal clinical follow-up, the patient suffered an acute myocardial infarction 3 years after this initial CT and died 12 years after CT at the age of 64 years. (C) Contrast-enhanced CT performed 7 months before death for minor trauma was interpreted as negative but does show significant progression of vascular calcification, visceral fat, and hepatic steatosis. HU=Hounsfield units.
A chest CT scan of a 79-year-old woman who presented with fever, dry cough, and chest pain for three days. Her husband and daughter-in-law had been recently diagnosed with coronavirus disease. The patient expired 11 days after admission.(Courtesy of Song F, Shanghai Public Health Clinical Center, Shanghai, China)