Deep learning image reconstruction for improving image quality of contrast-enhanced dual-energy CT in abdomen

Objectives To evaluate the usefulness of deep learning image reconstruction (DLIR) to improve the image quality of dual-energy computed tomography (DECT) of the abdomen, compared to hybrid iterative reconstruction (IR). Methods This study included 40 patients who underwent contrast-enhanced DECT of the abdomen. Virtual monochromatic 40-, 50-, and 70-keV and iodine density images were reconstructed using three reconstruction algorithms, including hybrid IR (ASiR-V50%) and DLIR (TrueFidelity) at medium- and high-strength level (DLIR-M and DLIR-H, respectively). The standard deviation of attenuation in liver parenchyma was measured as image noise. The contrast-to-noise ratio (CNR) for the portal vein on portal venous phase CT was calculated. The vessel conspicuity and overall image quality were graded on a 5-point scale ranging from 1 (poor) to 5 (excellent). The comparative scale of lesion conspicuity in 47 abdominal solid lesions was evaluated on a 5-point scale ranging from 0 (best) to -4 (markedly inferior). Results The image noise of virtual monochromatic 40-, 50 -, and 70-keV and iodine density images was significantly decreased by DLIR compared to hybrid IR (p < 0.0001). The CNR was significantly higher in DLIR-H and DLIR-M than in hybrid IR (p < 0.0001). The vessel conspicuity and overall image quality scores were also significantly greater in DLIR-H and DLIR-M than in hybrid IR (p < 0.05). The lesion conspicuity scores for DLIR-M and DLIR-H were significantly higher than those for hybrid IR in the virtual monochromatic image of all energy levels (p <= 0.001). Conclusions DLIR improves vessel conspicuity, CNR, and lesion conspicuity of virtual monochromatic and iodine density images in abdominal contrast-enhanced DECT, compared to hybrid IR.