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.
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Abbreviations
- AFP:
-
Serum alpha-fetoprotein
- AP:
-
Arterial phase
- CT:
-
Computed tomography
- DL-CB:
-
Deep-learning-based iodine contrast boosting
- DLD:
-
Double-low dose
- DP:
-
Delayed phase
- DRI:
-
Dose right index
- HCC:
-
Hepatocellular carcinoma
- IMR:
-
Iterative model reconstruction
- PVP:
-
Portal venous phase
- SD:
-
Standard-dose
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Acknowledgements
We thank ClariPI, Ltd. (Seoul, Korea) for providing technical support for the DLICB algorithm. However, the authors maintained complete control of the data and the information submitted for publication at all times.
Funding
This work was supported by TAEJOON Pharm Co., Ltd. (Seoul, Korea) and the Korea Medical Device Development Fund grant funded by the Korean government (the Ministry of Science and ICT, the Ministry of Trade, Industry, and Energy, the Ministry of Health & Welfare, the Ministry of Food and Drug Safety) (RS-2020-KD000226, 1711174549).
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The scientific guarantor of this publication is Jeong Min Lee.
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Medical Research Collaborating Center (MRCC) of Seoul National University Medical School/Hospital kindly provided statistical advice for this manuscript.
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Written informed consent was obtained from all subjects (patients) in this study.
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Kang, HJ., Lee, J.M., Ahn, C. et al. 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. Eur Radiol 33, 3660–3670 (2023). https://doi.org/10.1007/s00330-023-09520-4
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DOI: https://doi.org/10.1007/s00330-023-09520-4