Department of Radiology, Seoul National University Hospital, 101 Daehangno, Jongno-Gu, Seoul, 03080, Korea.
Department of Radiology, Seoul National University College of Medicine, Seoul, Korea.
Eur Radiol. 2023 May;33(5):3660-3670. doi: 10.1007/s00330-023-09520-4. Epub 2023 Mar 18.
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.
探讨深度学习对比增强(DL-CB)算法在同时降低肝癌(HCC)高危患者辐射和对比剂剂量的双低剂量(DLD)CT 中的图像质量和病变显示情况。
招募参与者并进行四期动态 CT(NCT04722120)。他们被随机分配到标准剂量(SD)或 DLD 方案。所有 CT 图像均采用迭代重建初始重建,DLD 方案的图像采用 DL-CB 算法(DLD-DL)进一步处理。主要终点是对比噪声比(CNR),次要终点是定性图像质量(噪声、肝病变和血管显示),次要终点是病变检出率。采用 t 检验或重复测量方差分析进行分析。
共纳入 68 名有 57 个局灶性肝病变的参与者(20 名 HCC 和 37 名良性病变)。DLD 方案的辐射剂量降低了 19.8%(DLP,855.1±254.8 mGy·cm 比 713.3±94.6 mGy·cm,p=0.003),对比剂剂量降低了 27%(106.9±15.0 mL 比 77.9±9.4 mL,p<0.001)。比较分析表明,DLD-DL 方案的 CNR(p<0.001)和门静脉显示(p=0.002)均明显高于 SD 方案。所有病变(82.7%比 73.3%,p=0.140)和 HCC(75.7%比 70.4%,p=0.644)的检出率在 SD 方案和 DLD-DL 之间无显著差异。
与标准剂量采集相比,双低剂量 CT 上的深度学习对比增强算法可提高主动脉和门静脉的 CNR,而不会显著降低 HCC 的检出率,即使是在 HCC 高危患者中。
与标准剂量 CT 相比,基于深度学习的对比增强算法在双低剂量 CT 上可提高对比噪声比。
标准剂量 CT 和双低剂量 CT 上的深度学习对比增强算法的肝局灶性病变检出率无显著差异。
无深度学习算法的双低剂量 CT 呈现较低的 CNR 和较差的图像质量。