调查基于深度学习的对比提升（DL-CB）算法对高风险肝细胞癌（HCC）参与者的双低剂量（DLD）CT图像质量和病变明显性的影响，实现辐射和对比剂剂量同时降低。参与者被招募并进行四相动态CT检查（NCT04722120）。他们被随机分配到标准剂量（SD）或DLD方案。所有CT图像最初使用迭代重构进行重建，而DLD方案的图像则进一步使用DL-CB算法进行处理（DLD-DL）。主要终点是对比度-噪声比（CNR），次要终点是定性图像质量（噪音、肝脏病变和血管明显性），三级终点是病变检出率。采用t检验或重复测量方差分析进行分析。共招募了68名参与者，其中57个为肝脏病变（20个HCC和37个良性病变）。DLD方案的辐射剂量降低了19.8%（DLP，855.1 ± 254.8 mGy·cm vs. 713.3 ± 94.6 mGy·cm，p = .003），对比剂剂量降低了27％（106.9 ± 15.0 mL vs. 77.9 ± 9.4 mL，p < .001），与SD方案相比。比较分析表明，DLD-DL的CNR（p < .001）和门静脉明显性（p = .002）均显著高于SD方案。对于所有病变（82.7% vs. 73.3%，p = .140）和HCCs（75.7% vs. 70.4%，p = .644），SD方案和DLD-DL之间的病变检出率没有显着差异。双低剂量CT上的DL-CB提供了改善主动脉和门静脉的CNR，而不会显着影响HCC的检出率，甚至对高风险HCC参与者也是如此。• 基于深度学习的对比提升算法可提供比标准剂量CT更好的对比度-噪声比。• Focal肝病变的检出率在标准剂量CT和基于深度学习的对比增强算法的双低剂量CT之间没有显着差异。• 没有深度学习算法的双低剂量CT具有较低的CNR和较差的图像质量。©2023. 作者，独家授权欧洲放射学会。

To investigate the image quality and lesion conspicuity of a deep-learning-based contrast-boosting (DL-CB) algorithm on double-low-dose (DLD) CT of simultaneous reduction of radiation and contrast doses in participants at high-risk for hepatocellular carcinoma (HCC).Participants were recruited and underwent four-phase dynamic CT (NCT04722120). They were randomly assigned to either standard-dose (SD) or DLD protocol. All CT images were initially reconstructed using iterative reconstruction, and the images of the DLD protocol were further processed using the DL-CB algorithm (DLD-DL). The primary endpoint was the contrast-to-noise ratio (CNR), the secondary endpoint was qualitative image quality (noise, hepatic lesion, and vessel conspicuity), and the tertiary endpoint was lesion detection rate. The t-test or repeated measures analysis of variance was used for analysis.Sixty-eight participants with 57 focal liver lesions were enrolled (20 with HCC and 37 with benign findings). The DLD protocol had a 19.8% lower radiation dose (DLP, 855.1 ± 254.8 mGy·cm vs. 713.3 ± 94.6 mGy·cm, p = .003) and 27% lower contrast dose (106.9 ± 15.0 mL vs. 77.9 ± 9.4 mL, p < .001) than the SD protocol. The comparative analysis demonstrated that CNR (p < .001) and portal vein conspicuity (p = .002) were significantly higher in the DLD-DL than in the SD protocol. There was no significant difference in lesion detection rate for all lesions (82.7% vs. 73.3%, p = .140) and HCCs (75.7% vs. 70.4%, p = .644) between the SD protocol and DLD-DL.DL-CB on double-low-dose CT provided improved CNR of the aorta and portal vein without significant impairment of the detection rate of HCC compared to the standard-dose acquisition, even in participants at high risk for HCC.• Deep-learning-based contrast-boosting algorithm on double-low-dose CT provided an improved contrast-to-noise ratio compared to standard-dose CT. • The detection rate of focal liver lesions was not significantly differed between standard-dose CT and a deep-learning-based contrast-boosting algorithm on double-low-dose CT. • Double-low-dose CT without a deep-learning algorithm presented lower CNR and worse image quality.© 2023. The Author(s), under exclusive licence to European Society of Radiology.