Spectral CT Helps Detect Early-Stage COVID-19

To do so, they examined data from spectral CT scans conducted in four patients with confirmed COVID-19. The spectral CT images were reconstructed using the standard soft kernel (filter B) and an iterative method used to capture conventional CT images. They then compared the initial conventional CT images with the follow-up ones.

All 45 GGOs identified in the four patients showed up better on the ED images than on the conventional initial CT scans, with the follow-up conventional CT scans confirming the presence of the GGOs. In addition, the results showed that lesion extent, as assessed using a semi-quantitative reporting scale denoting surface area involvement for each lobe, was much easier to determine on the ED images. The study was published on October 21, 2020, in the American Journal of Roentgenology.

“ED imaging may improve the detection of early-stage COVID-19, a stage for which conventional CT has shown limited sensitivity for detection. Spectral CT may also provide a better assessment of the extent of the lung lesions,” said lead author Beatrice Daoud, MD, of the department of radiology. “This study marks the first time investigators have looked into whether spectral CT scans can improve providers’ abilities to assess lung lesion extent.”

Spectral CT, also known as dual-energy CT, measures tissue attenuation at two different energy levels, allowing computation of the two physical effects responsible for x-ray attenuation, the photoelectric effect and Compton scatter. Multiple spectral images can be created that show the attenuation that would result from a monochromatic x-ray source, iodine maps, effective atomic number maps, and ED maps. These spectral data have been shown to improve contrast enhancement, reduce artifacts, and better characterize tissues.

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