Skip to main content
SpringerLink
Log in

Artificial intelligence–based full aortic CT angiography imaging with ultra-low-dose contrast medium: a preliminary study

  • Computed Tomography
  • Published:
European Radiology Aims and scope Submit manuscript

A Correction to this article was published on 15 November 2022

This article has been updated

Abstract

Objectives

To further reduce the contrast medium (CM) dose of full aortic CT angiography (ACTA) imaging using the augmented cycle-consistent adversarial framework (Au-CycleGAN) algorithm.

Methods

We prospectively enrolled 150 consecutive patients with suspected aortic disease. All received ACTA scans of ultra-low-dose CM (ULDCM) protocol and low-dose CM (LDCM) protocol. These data were randomly assigned to the training datasets (n = 100) and the validation datasets (n = 50). The ULDCM images were reconstructed by the Au-CycleGAN algorithm. Then, the AI-based ULDCM images were compared with LDCM images in terms of image quality and diagnostic accuracy.

Results

The mean image quality score of each location in the AI-based ULDCM group was higher than that in the ULDCM group but a little lower than that in the LDCM group (all p < 0.05). All AI-based ULDCM images met the diagnostic requirements (score ≥ 3). Except for the image noise, the AI-based ULDCM images had higher attenuation value than the ULDCM and LDCM images as well as higher SNR and CNR in all locations of the aorta analyzed (all p < 0.05). Similar results were also seen in obese patients (BMI > 25, all p < 0.05). Using the findings of LDCM images as the reference, the AI-based ULDCM images showed good diagnostic parameters and no significant differences in any of the analyzed aortic disease diagnoses (all K-values > 0.80, p < 0.05).

Conclusions

The required dose of CM for full ACTA imaging can be reduced to one-third of the CM dose of the LDCM protocol while maintaining image quality and diagnostic accuracy using the Au-CycleGAN algorithm.

Key Points

• The required dose of contrast medium (CM) for full ACTA imaging can be reduced to one-third of the CM dose of the low-dose contrast medium (LDCM) protocol using the Au-CycleGAN algorithm.

• Except for the image noise, the AI-based ultra-low-dose contrast medium (ULDCM) images had better quantitative image quality parameters than the ULDCM and LDCM images.

• No significant diagnostic differences were noted between the AI-based ULDCM and LDCM images regarding all the analyzed aortic disease diagnoses.

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

Similar content being viewed by others

Change history

Abbreviations

ACTA:

Aortic CT angiography

AI:

Artificial intelligence

ASIR-V:

Adaptive statistical iterative reconstruction-V

Au-CycleGAN:

Augmented cycle-consistent adversarial framework

BMI :

Body mass index

CNR:

Contrast-to-noise ratio

DLIR:

Deep learning image reconstruction

LDCM:

Low-dose contrast medium

SNR:

Signal-to-noise ratio

ULDCM:

Ultra-low-dose contrast medium

References

  1. Erbel R, Aboyans V, Boileau C et al (2014) 2014 ESC guidelines on the diagnosis and treatment of aortic diseases: document covering acute and chronic aortic diseases of the thoracic and abdominal aorta of the adult. The Task Force for the Diagnosis and Treatment of Aortic Diseases of the European Society of Cardiology (ESC). Eur Heart J 35:2873–2926

    Article  Google Scholar 

  2. Knuuti J, Bengel F, Bax JJ et al (2014) Risks and benefits of cardiac imaging: an analysis of risks related to imaging for coronary artery disease. Eur Heart J 35:633–638

    Article  Google Scholar 

  3. Masuda T, Funama Y, Nakaura T et al (2018) Radiation dose reduction with a low-tube voltage technique for pediatric chest computed tomographic angiography based on the contrast-to-noise ratio index. Can Assoc Radiol J 69:390–396

    Article  Google Scholar 

  4. Lenga L, Albrecht MH, Othman AE et al (2017) Monoenergetic dual-energy computed tomographic imaging: cardiothoracic applications. J Thorac Imaging 32:151–158

    Article  Google Scholar 

  5. Benz DC, Grani C, Hirt Moch B et al (2016) Minimized radiation and contrast agent exposure for coronary computed tomography angiography: first clinical experience on a latest generation 256-slice scanner. Acad Radiol 23:1008–1014

    Article  Google Scholar 

  6. Albrecht MH, De Cecco CN, Schoepf UJ et al (2018) Dual-energy CT of the heart current and future status. Eur J Radiol 105:110–118

    Article  Google Scholar 

  7. Schmidt B, Flohr T (2020) Principles and applications of dual source CT. Phys Med 79:36–46

    Article  Google Scholar 

  8. Kahn CE Jr (2017) From images to actions: opportunities for artificial intelligence in radiology. Radiology 285:719–720

    Article  Google Scholar 

  9. Anthimopoulos M, Christodoulidis S, Ebner L, Christe A, Mougiakakou S (2016) Lung pattern classification for interstitial lung diseases using a deep convolutional neural network. IEEE Trans Med Imaging 35:1207–1216

    Article  Google Scholar 

  10. Baskaran L, Maliakal G, Al'Aref SJ et al (2020) Identification and quantification of cardiovascular structures from CCTA: an end-to-end, rapid, pixel-wise, deep-learning method. JACC Cardiovasc Imaging 13:1163–1171

    Article  Google Scholar 

  11. Zhang N, Yang G, Zhang W et al (2021) Fully automatic framework for comprehensive coronary artery calcium scores analysis on non-contrast cardiac-gated CT scan: total and vessel-specific quantifications. Eur J Radiol 134:109420

    Article  Google Scholar 

  12. van Velzen SGM, Lessmann N, Velthuis BK et al (2020) Deep learning for automatic calcium scoring in CT: validation using multiple cardiac CT and chest CT protocols. Radiology 295:66–79

    Article  Google Scholar 

  13. Nakanishi R, Slomka PJ, Rios R et al (2021) Machine learning adds to clinical and CAC assessments in predicting 10-year CHD and CVD deaths. JACC Cardiovasc Imaging 14:615–625

    Article  Google Scholar 

  14. Eisenberg E, McElhinney PA, Commandeur F et al (2020) Deep learning-based quantification of epicardial adipose tissue volume and attenuation predicts major adverse cardiovascular events in asymptomatic subjects. Circ Cardiovasc Imaging 13:e009829

    Article  Google Scholar 

  15. Yang Q, Yan P, Zhang Y et al (2018) Low-dose CT image denoising using a generative adversarial network with Wasserstein distance and perceptual loss. IEEE Trans Med Imaging 37:1348–1357

    Article  Google Scholar 

  16. 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  Google Scholar 

  17. Benz DC, Benetos G, Rampidis G et al (2020) Validation of deep-learning image reconstruction for coronary computed tomography angiography: impact on noise, image quality and diagnostic accuracy. J Cardiovasc Comput Tomogr 14:444–451

    Article  Google Scholar 

  18. Brady SL, Kaufman RA (2012) Investigation of American Association of Physicists in Medicine Report 204 size-specific dose estimates for pediatric CT implementation. Radiology 265:832–840

    Article  Google Scholar 

  19. Shrimpton PC, Hillier MC, Lewis MA, Dunn M (2006) National survey of doses from CT in the UK: 2003. Br J Radiol 79:968–980

    Article  CAS  Google Scholar 

  20. Liu T, Gao Y, Wang H et al (2020) Association between right ventricular strain and outcomes in patients with dilated cardiomyopathy. Heart. https://doi.org/10.1136/heartjnl-2020-317949

  21. Nwabuo CC, Moreira HT, Vasconcellos HD et al (2019) Left ventricular global function index predicts incident heart failure and cardiovascular disease in young adults: the coronary artery risk development in young adults (CARDIA) study. Eur Heart J Cardiovasc Imaging 20:533–540

    Article  Google Scholar 

  22. Karnik AA, Gopal DM, Ko D, Benjamin EJ, Helm RH (2019) Epidemiology of atrial fibrillation and heart failure: a growing and important problem. Cardiol Clin 37:119–129

    Article  Google Scholar 

  23. Januzzi JL Jr, Prescott MF, Butler J et al (2019) Association of change in N-terminal pro-B-type natriuretic peptide following initiation of sacubitril-valsartan treatment with cardiac structure and function in patients with heart failure with reduced ejection fraction. JAMA 322:1085–1095

    Article  CAS  Google Scholar 

  24. Desai AS, Solomon SD, Shah AM et al (2019) Effect of sacubitril-valsartan vs enalapril on aortic stiffness in patients with heart failure and reduced ejection fraction: a randomized clinical trial. JAMA 322:1077–1084

    Article  CAS  Google Scholar 

  25. Stewart MH, Lavie CJ, Shah S et al (2018) Prognostic implications of left ventricular hypertrophy. Prog Cardiovasc Dis 61:446–455

    Article  Google Scholar 

  26. Liu T, Song D, Dong J et al (2017) Current understanding of the pathophysiology of myocardial fibrosis and its quantitative assessment in heart failure. Front Physiol 8:238

    Article  Google Scholar 

  27. Ippolito D, Talei Franzesi C, Fior D, Bonaffini PA, Minutolo O, Sironi S (2015) Low kV settings CT angiography (CTA) with low dose contrast medium volume protocol in the assessment of thoracic and abdominal aorta disease: a feasibility study. Br J Radiol 88:20140140

    Article  CAS  Google Scholar 

  28. Wei L, Li S, Gao Q, Liu Y, Ma X (2016) Use of low tube voltage and low contrast agent concentration yields good image quality for aortic CT angiography. Clin Radiol 71:1313 e1315-1313 e1310

  29. Piechowiak EI, Peter JF, Kleb B, Klose KJ, Heverhagen JT (2015) Intravenous iodinated contrast agents amplify DNA radiation damage at CT. Radiology 275:692–697

    Article  Google Scholar 

  30. Geyer LL, Schoepf UJ, Meinel FG et al (2015) State of the art: iterative CT reconstruction techniques. Radiology 276:339–357

    Article  Google Scholar 

  31. Johansen CB, Martinsen ACT, Enden TR, Svanteson M (2022) The potential of iodinated contrast reduction in dual-energy CT thoracic angiography; an evaluation of image quality. Radiography (Lond) 28:2–7

    Article  CAS  Google Scholar 

  32. Albrecht MH, Trommer J, Wichmann JL et al (2016) Comprehensive comparison of virtual monoenergetic and linearly blended reconstruction techniques in third-generation dual-source dual-energy computed tomography angiography of the thorax and abdomen. Invest Radiol 51:582–590

    Article  CAS  Google Scholar 

  33. Yi Y, Zhao XM, Wu RZ et al (2019) Low dose and low contrast medium coronary CT angiography using dual-layer spectral detector CT. Int Heart J 60:608–617

    Article  Google Scholar 

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

    Article  Google Scholar 

  35. Sun J, Li H, Gao J et al (2021) Performance evaluation of a deep learning image reconstruction (DLIR) algorithm in “double low” chest CTA in children: a feasibility study. Radiol Med 126:1181–1188

    Article  Google Scholar 

  36. 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  Google Scholar 

  37. Hagan PG, Nienaber CA, Isselbacher EM et al (2000) The International Registry of Acute Aortic Dissection (IRAD): new insights into an old disease. JAMA 283:897–903

    Article  CAS  Google Scholar 

  38. Parker MS, Matheson TL, Rao AV et al (2001) Making the transition: the role of helical CT in the evaluation of potentially acute thoracic aortic injuries. AJR Am J Roentgenol 176:1267–1272

    Article  CAS  Google Scholar 

  39. Quint LE, Francis IR, Williams DM et al (1996) Evaluation of thoracic aortic disease with the use of helical CT and multiplanar reconstructions: comparison with surgical findings. Radiology 201:37–41

    Article  CAS  Google Scholar 

  40. Einstein AJ, Weiner SD, Bernheim A et al (2010) Multiple testing, cumulative radiation dose, and clinical indications in patients undergoing myocardial perfusion imaging. JAMA 304:2137–2144

    Article  CAS  Google Scholar 

Download references

Acknowledgements

We are grateful to all the staff and epidemiologists who were involved in the care of our study subjects.

Funding

This study has received funding from the National Natural Science Foundation of China (82271986, U1908211), the Capital’s Funds for Health Improvement and Research Foundation of China (2020-1-1052), and the National Key Research and Development Program of China (2016YFC1300300).

Author information

Author notes

  1. Zhen Zhou and Yifeng Gao contributed equally to this work and share first authorship.

Authors and Affiliations

  1. Department of Radiology, Beijing Anzhen Hospital, Capital Medical University, No. 2, Anzhen Road, Chaoyang District, Beijing, 100029, China

    Zhen Zhou, Yifeng Gao, Kairui Bo, Nan Zhang, Hui Wang, Rui Wang, Zhiqiang Du & Lei Xu

  2. School of Biomedical Engineering, Sun Yat-Sen University, Guangzhou, China

    Weiwei Zhang & Heye Zhang

  3. Cardiovascular Research Centre, Royal Brompton Hospital, London, SW3 6NP, UK

    David Firmin & Guang Yang

  4. National Heart and Lung Institute, Imperial College London, London, SW7 2AZ, UK

    David Firmin & Guang Yang

Authors
  1. Zhen Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yifeng Gao
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Weiwei Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Kairui Bo
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Nan Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Hui Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Rui Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Zhiqiang Du
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. David Firmin
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Guang Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Heye Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Lei Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Lei Xu.

Ethics declarations

Guarantor

The scientific guarantor of this publication is Lei Xu.

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

One of the authors (Nan Zhang) has significant statistical expertise.

Informed consent

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

Ethical approval

Institutional Review Board approval was obtained.

Methodology

• prospective

• diagnostic or prognostic study

• performed at one institution

Additional information

Publisher’s note

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

The original online version of this article was revised: the funding information was corrected.

Supplementary Information

ESM 1

(DOCX 374 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

Zhou, Z., Gao, Y., Zhang, W. et al. Artificial intelligence–based full aortic CT angiography imaging with ultra-low-dose contrast medium: a preliminary study. Eur Radiol 33, 678–689 (2023). https://doi.org/10.1007/s00330-022-08975-1

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00330-022-08975-1

Keywords

Search

Navigation