IMAGE: B, Representative computational model shows lung stroma intraorgan structure of XCAT phantom that was developed using anatomically informed mathematic model. Inset shows enlarged view for better visibility of details and…
Leesburg, VA, August 25, 2020–An open-access article in ARRS’ American Journal of Roentgenology (AJR) 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 coronavirus disease (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 this 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 explained, “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 et al. continued.
Using a specific CT scanner (Definition Flash, Siemens Healthineers) and validated radiography simulator (DukeSim) to help illustrate utility, the team virtually imaged three developed COVID-19 computational phantoms.
“Subjectively,” the authors concluded, “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.
We allow third-party companies to serve ads and/or collect anonymous information. These companies may use non-personally identifiable information (browser type, time and date) in order to provide advertisements about goods and services likely to be of greater interest to you. These companies typically use a cookie or third party web beacon to collect this information. To learn more about this behavioral advertising practice or to opt-out of this type of advertising, please visit networkadvertising.org.
World news – US – Virtual imaging trials optimize CT, radiography for coronavirus disease (COVID-19)