Assessment of hepatic steatosis based on virtual non-contrast computed tomography: Initial experiences with a photon counting scanner approved for clinical use

Purpose: The present study evaluates the diagnostic accuracy of virtual non-contrast (VNC) images acquired with the first clinical photon counting CT scanner for the assessment of hepatic steatosis.Method: 140 patients were retrospectively enrolled in this study. The following CT indices were calculated according to the attenuation on VNC and true non-contrast (TNC) images: Liver attenuation CT (L), difference between liver and spleen attenuation CT (L-S) and the ratio between liver and spleen attenuation CT (L/S).Results: Considering previously reported cut-off values, the sensitivity, specificity, positive and negative predictive value (PPV, NPV) for the detection of hepatic steatosis on VNC images was 94%, 87%, 30% and 99.5% for CT (L), 52%, 99%, 81% and 96% for CT (L-S) and 67%, 98%, 74% and 94% for CT (L/S). When adjusting the cut-off values according to the present study, the sensitivity, specificity, PPV and NPV for the detection of hepatic steatosis on VNC images was 94%, 92%, 41% and 99.6% for CT (L), 96%, 90%, 46% and 99.6% for CT (L-S) and 95%, 99.6%, 42% and 99.6% for CT (L/S).Conclusions: In conclusion, the spectral datasets acquired with the first clinical PCCT scanner enable the reconstruction of VNC images that are reliable for the image-based assessment of hepatic steatosis. By adjusting the cut-off values all tested CT indices perform well and offer an excellent sensitivity as well as specificity.

Automated summary

The study evaluated the diagnostic accuracy of virtual non-contrast images acquired with the first clinical photon counting CT scanner for the assessment of hepatic steatosis. The results showed that the spectral datasets from the PCCT scanner allowed reliable reconstruction of VNC images for accurate evaluation of hepatic steatosis. Adjusting the cut-off values improved the performance of the CT indices, offering excellent sensitivity and specificity.