Skip to main content
SpringerLink
Log in

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

  • Gastrointestinal
  • Published:
European Radiology Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

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

References

  1. Park W, Chung YH, Kim JA et al (2013) Recurrences of hepatocellular carcinoma following complete remission by transarterial chemoembolization or radiofrequency therapy: focused on the recurrence patterns. Hepatol Res 43:1304–1312

    Article  PubMed  Google Scholar 

  2. Yang W, Yan K, Goldberg SN et al (2016) Ten-year survival of hepatocellular carcinoma patients undergoing radiofrequency ablation as a first-line treatment. World J Gastroenterol 22:2993

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Liu D, Fong DY, Chan AC, Poon RT, Khong P-L (2015) Hepatocellular carcinoma: surveillance CT schedule after hepatectomy based on risk stratification. Radiology 274:133–140

    Article  PubMed  Google Scholar 

  4. Boas FE, Do B, Louie JD et al (2015) Optimal imaging surveillance schedules after liver-directed therapy for hepatocellular carcinoma. J Vasc Interv Radiol 26:69–73

    Article  PubMed  Google Scholar 

  5. Nam CY, Chaudhari V, Raman SS et al (2011) CT and MRI improve detection of hepatocellular carcinoma, compared with ultrasound alone, in patients with cirrhosis. Clin Gastroenterol Hepatol 9:161–167

    Article  Google Scholar 

  6. Hanna RF, Miloushev VZ, Tang A et al (2016) Comparative 13-year meta-analysis of the sensitivity and positive predictive value of ultrasound, CT, and MRI for detecting hepatocellular carcinoma. Abdom Radiol (NY) 41:71–90

    Article  PubMed  Google Scholar 

  7. Lima PH, Fan B, Bérubé J et al (2019) Cost-utility analysis of imaging for surveillance and diagnosis of hepatocellular carcinoma. AJR Am J Roentgenol 213:17–25

    Article  PubMed  Google Scholar 

  8. Haubold J, Hosch R, Umutlu L et al (2021) Contrast agent dose reduction in computed tomography with deep learning using a conditional generative adversarial network. Eur Radiol 31:6087–6095

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Park S-J, Kang D-Y, Sohn K-H et al (2018) Immediate mild reactions to CT with iodinated contrast media: strategy of contrast media readministration without corticosteroids. Radiology 288:710–716

    Article  PubMed  Google Scholar 

  10. Lee S-Y, Yang MS, Choi Y-H et al (2017) Stratified premedication strategy for the prevention of contrast media hypersensitivity in high-risk patients. Ann Allergy Asthma Immunol 118:339-344 e331

    Article  PubMed  Google Scholar 

  11. Hunt CH, Hartman RP, Hesley GK (2009) Frequency and severity of adverse effects of iodinated and gadolinium contrast materials: retrospective review of 456,930 doses. AJR Am J Roentgenol 193:1124–1127

    Article  PubMed  Google Scholar 

  12. Yoon JH, Lee JM, Yu MH et al (2014) Comparison of iterative model–based reconstruction versus conventional filtered Back projection and hybrid iterative reconstruction techniques: lesion conspicuity and influence of body size in anthropomorphic liver phantoms. J Comput Assist Tomogr 38:859–868

    Article  PubMed  Google Scholar 

  13. Willemink MJ, Noël PB (2019) The evolution of image reconstruction for CT—from filtered back projection to artificial intelligence. Eur Radiol 29:2185–2195

    Article  PubMed  Google Scholar 

  14. Chang W, Lee JM, Lee K et al (2013) Assessment of a model-based, iterative reconstruction algorithm (MBIR) regarding image quality and dose reduction in liver computed tomography. Invest Radiol 48:598–606

    Article  CAS  PubMed  Google Scholar 

  15. Mayo-Smith WW, Hara AK, Mahesh M, Sahani DV, Pavlicek W (2014) How I do it: managing radiation dose in CT. Radiology 273:657–672

    Article  PubMed  Google Scholar 

  16. Laurent G, Villani N, Hossu G et al (2019) Full model-based iterative reconstruction (MBIR) in abdominal CT increases objective image quality, but decreases subjective acceptance. Eur Radiol 29:4016–4025

    Article  PubMed  Google Scholar 

  17. Solomon J, Lyu P, Marin D, Samei E (2020) Noise and spatial resolution properties of a commercially available deep learning-based CT reconstruction algorithm. Med Phys 47:3961–3971

    Article  PubMed  Google Scholar 

  18. Park S, Yoon JH, Joo I et al (2021) Image quality in liver CT: low-dose deep learning vs standard-dose model-based iterative reconstructions. Eur Radiol 32:2865–2574

    Article  PubMed  Google Scholar 

  19. Chartrand G, Cheng PM, Vorontsov E et al (2017) Deep learning: a primer for radiologists. Radiographics 37:2113–2131

    Article  PubMed  Google Scholar 

  20. Kim JH, Yoon HJ, Lee E, Kim I, Cha YK, Bak SH (2021) Validation of deep-learning image reconstruction for low-dose chest computed tomography scan: Emphasis on image quality and noise. Korean J Radiol 22:131

    Article  PubMed  Google Scholar 

  21. Greffier J, Hamard A, Pereira F et al (2020) Image quality and dose reduction opportunity of deep learning image reconstruction algorithm for CT: a phantom study. Eur Radiol 30:3951–3959

    Article  PubMed  Google Scholar 

  22. Singh R, Digumarthy SR, Muse VV et al (2020) Image quality and lesion detection on deep learning reconstruction and iterative reconstruction of submillisievert chest and abdominal CT. AJR Am J Roentgenol 214:566–573

    Article  PubMed  Google Scholar 

  23. Park C, Choo KS, Jung Y, Jeong HS, Hwang J-Y, Yun MS (2021) CT iterative vs deep learning reconstruction: comparison of noise and sharpness. Eur Radiol 31:3156–3164

    Article  PubMed  Google Scholar 

  24. Chernyak V, Fowler KJ, Kamaya A et al (2018) Liver Imaging Reporting and Data System (LI-RADS) version 2018: imaging of hepatocellular carcinoma in at-risk patients. Radiology 289:816–830

    Article  PubMed  Google Scholar 

  25. Park HJ, Lee JM, Park SB, Lee JB, Jeong YK, Yoon JH (2016) Comparison of knowledge-based iterative model reconstruction and hybrid reconstruction techniques for liver CT evaluation of hypervascular hepatocellular carcinoma. J Comput Assist Tomogr 40:863–871

    Article  PubMed  Google Scholar 

  26. Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentationInternational Conference on Medical image computing and computer-assisted intervention. Springer pp 234–241

  27. Won Kim C, Kim JH (2014) Realistic simulation of reduced-dose CT with noise modeling and sinogram synthesis using DICOM CT images. Med Phys 41:011901

    Article  PubMed  Google Scholar 

  28. Jin H, Heo C, Kim JH (2019) Deep learning-enabled accurate normalization of reconstruction kernel effects on emphysema quantification in low-dose CT. Phys Med Biol 64:135010

    Article  PubMed  Google Scholar 

  29. Isola P, Zhu J-Y, Zhou T, Efros AA (2017) Image-to-image translation with conditional adversarial networks. Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1125–1134

  30. Kang H-J, Lee JM, Lee SM et al (2019) Value of virtual monochromatic spectral image of dual-layer spectral detector CT with noise reduction algorithm for image quality improvement in obese simulated body phantom. BMC Med Imaging 19:76

    Article  PubMed  PubMed Central  Google Scholar 

  31. Yoon JH, Chang W, Lee ES, Lee SM, Lee JM (2020) Double low-dose dual-energy liver CT in patients at high-risk of HCC: a prospective, randomized, single-center study. Invest Radiol 55:340–348

    Article  CAS  PubMed  Google Scholar 

  32. Elsayes KM, Kielar AZ, Elmohr MM et al (2018) White paper of the Society of Abdominal Radiology hepatocellular carcinoma diagnosis disease-focused panel on LI-RADS v2018 for CT and MRI. Abdom Radiol (NY) 43:2625–2642

    Article  PubMed  Google Scholar 

  33. Alkadhi H, Euler A (2020) The future of computed tomography: personalized, functional, and precise. Invest Radiol 55:545–555

    Article  PubMed  Google Scholar 

  34. Akagi M, Nakamura Y, Higaki T et al (2019) Deep learning reconstruction improves image quality of abdominal ultra-high-resolution CT. Eur Radiol 29:6163–6171

    Article  PubMed  Google Scholar 

  35. Nakamura Y, Higaki T, Tatsugami F et al (2020) Possibility of deep learning in medical imaging focusing improvement of computed tomography image quality. J Comput Assist Tomogr 44:161–167

    Article  PubMed  Google Scholar 

  36. Grosse Hokamp N, Hoink AJ, Doerner J et al (2018) Assessment of arterially hyper-enhancing liver lesions using virtual monoenergetic images from spectral detector CT: phantom and patient experience. Abdom Radiol (NY) 43:2066–2074

    Article  CAS  PubMed  Google Scholar 

  37. Matsuda M, Tsuda T, Kido T et al (2018) Dual-energy computed tomography in patients with small hepatocellular carcinoma: utility of noise-reduced monoenergetic images for the evaluation of washout and image quality in the equilibrium phase. J Comput Assist Tomogr 42:937–943

    Article  PubMed  Google Scholar 

Download references

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).

Author information

Authors and Affiliations

  1. Department of Radiology, Seoul National University Hospital, 101 Daehangno, Jongno-Gu, Seoul, 03080, Korea

    Hyo-Jin Kang, Jeong Min Lee, Jae Seok Bae, Jeong Hee Yoon & Joon Koo Han

  2. Department of Radiology, Seoul National University College of Medicine, Seoul, Korea

    Hyo-Jin Kang, Jeong Min Lee, Jae Seok Bae, Jeong Hee Yoon & Joon Koo Han

  3. Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, Korea

    Jeong Min Lee & Joon Koo Han

  4. Department of Transdisciplinary Studies, Program in Biomedical Radiation Sciences, Graduate School of Convergence Science and Technology, Seoul National University, Seoul, Korea

    Chulkyun Ahn

  5. Research Institute, ClariPi, Seoul, Korea

    Chulkyun Ahn

  6. Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea

    Seungchul Han

  7. Department of Radiology, Armed Forces Daejeon Hospital, Daejeon, Korea

    Se Woo Kim

Authors
  1. Hyo-Jin Kang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jeong Min Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Chulkyun Ahn
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jae Seok Bae
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Seungchul Han
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Se Woo Kim
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Jeong Hee Yoon
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Joon Koo Han
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jeong Min Lee.

Ethics declarations

Guarantor

The scientific guarantor of this publication is Jeong Min Lee.

Conflict of interest

The authors of this manuscript declare no relationships with any companies whose products or services may be related to the subject matter of the article.

Statistics and biometry

Medical Research Collaborating Center (MRCC) of Seoul National University Medical School/Hospital kindly provided statistical advice for this manuscript.

Informed consent

Written informed consent was obtained from all subjects (patients) in this study.

Ethical approval

Institutional Review Board approval was obtained.

Methodology

• prospective

• randomised controlled trial

• performed at one institution

Additional information

Publisher's note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (DOCX 17.1 KB)

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

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

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00330-023-09520-4

Keywords

Search

Navigation