Virtual Imaging Trials for Coronavirus Disease (COVID-19).

Published online

Journal Article

OBJECTIVE. The virtual imaging trial is a unique framework that can greatly facilitate the assessment and optimization of imaging methods by emulating the imaging experiment using representative computational models of patients and validated imaging simulators. The purpose of this study was to show how virtual imaging trials can be adapted for imaging studies of coronavirus disease (COVID-19), enabling effective assessment and optimization of CT and radiography acquisitions and analysis tools for reliable imaging and management of COVID-19. MATERIALS AND METHODS. We developed the first computational models of patients with COVID-19 and as a proof of principle showed how they can be combined with imaging simulators for COVID-19 imaging studies. For the body habitus of the models, we used the 4D extended cardiac-torso (XCAT) model that was developed at Duke University. The morphologic features of COVID-19 abnormalities were segmented from 20 CT images of patients who had been confirmed to have COVID-19 and incorporated 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. To show the utility, three developed COVID-19 computational phantoms were virtually imaged using a scanner-specific CT and radiography simulator. RESULTS. Subjectively, the simulated abnormalities were realistic in terms of shape and texture. 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. CONCLUSION. The developed toolsets in this study provide the foundation for 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.

Full Text

Duke Authors

Cited Authors

  • Abadi, E; Paul Segars, W; Chalian, H; Samei, E

Published Date

  • August 21, 2020

Published In

Start / End Page

  • 1 - 7

PubMed ID

  • 32822224

Pubmed Central ID

  • 32822224

Electronic International Standard Serial Number (EISSN)

  • 1546-3141

Digital Object Identifier (DOI)

  • 10.2214/AJR.20.23429

Language

  • eng

Conference Location

  • United States