Researchers have developed virtual models to be used with imaging simulators to create more effective, inexpensive ways to assess imaging protocols for COVID-19 diagnosis, reports Physics World.
Medical imaging devices and methods are constantly evolving to meet rapidly changing clinical needs. CT and chest radiography, for example, could provide a means to detect lung abnormalities related to COVID-19, but the imaging parameters required to differentiate COVID-19 still need optimization. Clinical imaging trials, however, are often expensive, time consuming and can lack ground truth. Virtual, or in silico, imaging trials offer an effective alternative by simulating patients and imaging systems.
A group of researchers from Duke University took the first steps towards applying this approach to the current pandemic by developing the first computational models of patients with COVID-19. In addition, they showed how these virtual models can be adapted and used together with imaging simulators for COVID-19 imaging studies. Their proof-of-principle result, published in the American Journal of Roentgenology, paves the way towards effective and inexpensive ways of assessing and optimizing imaging protocols for rapid COVID-19 diagnosis.
Read more in Physics World.
Featured image: Simulated CT images of a COVID-19 XCAT phantom at three different radiation dose levels, representing (top left) 50, (top right) 25 and (lower left) 5 mAs settings. The lower right image shows a chest radiographic image of the same model. (Courtesy: American Roentgen Ray Society, American Journal of Roentgenology)
