An open-access article in the American Journal of Roentgenology established a foundation for the use of virtual imaging trials in effective assessment and optimization of CT and radiography acquisitions and analysis tools to help manage the COVID-19 pandemic.

Virtual imaging trials have two main components—representative models of targeted subjects and realistic models of imaging scanners—and the authors of the AJR article developed the first computational models of patients with COVID-19, while showing, as proof of principle, how they can be combined with imaging simulators for COVID-19 imaging studies.

“For the body habitus of the models,” lead author Ehsan Abadi, PhD, explains, “we used the 4D extended cardiac-torso (XCAT) model that was developed at Duke University.” Abadi and his Duke colleagues then segmented the morphologic features of COVID-19 abnormalities from 20 CT images of patients with multidiagnostic confirmation of SARS-CoV-2 infection and incorporated them into XCAT models.

“Within a given disease area, the texture and material of the lung parenchyma in the XCAT were modified to match the properties observed in the clinical images,” Abadi and his coauthors say. Using Siemens Healthineers’ Definition Flash CT scanner and DukeSim’s validated radiography simulator to help illustrate utility, the team virtually imaged three developed COVID-19 computational phantoms.

“Subjectively,” the authors conclude, “the simulated abnormalities were realistic in terms of shape and texture,” adding their preliminary results showed that the contrast-to-noise ratios in the abnormal regions were 1.6, 3.0, and 3.6 for 5-, 25-, and 50-mAs images, respectively.