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低剂量对比剂和基于深度学习的对比增强模型在肝细胞癌高危人群中的低辐射肝脏计算机断层扫描:前瞻性、随机、双盲研究。

Low dose of contrast agent and low radiation liver computed tomography with deep-learning-based contrast boosting model in participants at high-risk for hepatocellular carcinoma: prospective, randomized, double-blind study.

机构信息

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.

出版信息

Eur Radiol. 2023 May;33(5):3660-3670. doi: 10.1007/s00330-023-09520-4. Epub 2023 Mar 18.

Abstract

OBJECTIVE

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).

METHODS

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.

RESULTS

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.

CONCLUSIONS

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.

KEY POINTS

• 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.

摘要

目的

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。

方法

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。

结果

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。

结论

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。

关键要点

  • 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。

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