Evaluation of Hypervascular Focal Liver Lesions Utilizing Virtual Monoenergetic Images from Third-Generation Dual-Source Dual-Energy Computed Tomography

Abstract

Abstract Objectives The purpose of our study was to evaluate the virtual monochromatic imaging in detecting hypervascular focal liver lesions in the late arterial phase with third-generation dual-source dual-energy computed tomography and to assess its image quality. Materials and Methods In our study, 80 patients were included. Contrast-enhanced images in the late arterial phase (in the dual-energy mode) were acquired and were post-processed in Syngo, via workstation, using Monoenergetic + software. Five sets of images, one polychromatic energy image (corresponding to 120 kVp single-energy image) and four virtual monoenergetic image (VMI) sets at 40, 50, 60, and 70 keV levels, were generated. All these images were analyzed both objectively and subjectively. The attenuation values were measured, and the contrast-to-noise ratio (CNR) of liver and tumor were measured and compared objectively in each dataset. Image noise, image contrast, and diagnostic confidence for liver lesion detection were analyzed subjectively using a five-point scale system. Statistical analysis was performed using Kolmogorov–Smirnov, analysis of variance, and Kruskal–Wallis tests. Results Among the VMI, maximum image noise was observed in the 40 keV image, with a gradual reduction in the image noise being noted with an increase in the VMI energy. The CNR of the hepatic parenchyma and the tumor gradually increased with a reduction in VMI energy from 70 to 40 keV. On subjective analysis, image contrast and image noise were observed to be more in low VMI datasets. In lesion detection, diagnostic confidence with an excellent confidence level was observed with a decrease in VMI energy. Conclusion VMI datasets of 40 to 70 keV from third-generation dual-source DECT provide superior diagnostic accuracy for detecting hypervascular liver lesions. Considering the image noise and lesion detection rate among the VMI datasets, 60 keV VMI is the most helpful dataset for increased liver lesion detection with good image quality.