Skip to main content
SpringerLink
Log in

Deep learning reconstruction CT for liver metastases: low-dose dual-energy vs standard-dose single-energy

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

A Commentary to this article was published on 04 August 2023

Abstract

Objectives

To assess image quality and liver metastasis detection of reduced-dose dual-energy CT (DECT) with deep learning image reconstruction (DLIR) compared to standard-dose single-energy CT (SECT) with DLIR or iterative reconstruction (IR).

Methods

In this prospective study, two groups of 40 participants each underwent abdominal contrast-enhanced scans with full-dose SECT (120-kVp images, DLIR and IR algorithms) or reduced-dose DECT (40- to 60-keV virtual monochromatic images [VMIs], DLIR algorithm), with 122 and 106 metastases, respectively. Groups were matched by age, sex ratio, body mass index, and cross-sectional area. Noise power spectrum of liver images and task-based transfer function of metastases were calculated to assess the noise texture and low-contrast resolution. The image noise, signal-to-noise ratios (SNR) of liver and portal vein, liver-to-lesion contrast-to-noise ratio (LLR), lesion conspicuity, lesion detection rate, and the subjective image quality metrics were compared between groups on 1.25-mm reconstructed images.

Results

Compared to 120-kVp images with IR, 40- and 50-keV VMIs with DLIR showed similar noise texture and LLR, similar or higher image noise and low-contrast resolution, improved SNR and lesion conspicuity, and similar or better perceptual image quality. When compared to 120-kVp images with DLIR, 50-keV VMIs with DLIR had similar low-contrast resolution, SNR, LLR, lesion conspicuity, and perceptual image quality but lower frequency noise texture and higher image noise. For the detection of hepatic metastases, reduced-dose DECT by 34% maintained observer lesion detection rates.

Conclusion

DECT assisted with DLIR enables a 34% dose reduction for detecting hepatic metastases while maintaining comparable perceptual image quality to full-dose SECT.

Clinical relevance statement

Reduced-dose dual-energy CT with deep learning image reconstruction is as accurate as standard-dose single-energy CT for the detection of liver metastases and saves more than 30% of the radiation dose.

Key Points

• The 40- and 50-keV virtual monochromatic images (VMIs) with deep learning image reconstruction (DLIR) improved lesion conspicuity compared with 120-kVp images with iterative reconstruction while providing similar or better perceptual image quality.

• The 50-keV VMIs with DLIR provided comparable perceptual image quality and lesion conspicuity to 120-kVp images with DLIR.

• The reduction of radiation by 34% by DLIR in low-keV VMIs is clinically sufficient for detecting low-contrast hepatic metastases.

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

CTDIvol:

Volumetric CT dose index

CT:

Computed tomography

DECT:

Dual-energy CT

DLIR:

Deep learning image reconstruction

DM:

Medium-strength deep learning image reconstruction

DH:

High-strength deep learning image reconstruction

FBP:

Filtered back projection

GEE:

Generalized estimating equations

IR:

Iterative reconstruction

NPS:

Noise power spectrum

TTF:

Task-based transfer function

SNR:

Signal-to-noise ratio

SECT:

Single-energy CT

VMIs:

Virtual monochromatic images

References

  1. Mileto A, Guimaraes LS, McCollough CH, Fletcher JG, Yu L (2019) State of the art in abdominal CT: the limits of iterative reconstruction algorithms. Radiology 293:491–503. https://doi.org/10.1148/radiol.2019191422

    Article  PubMed  Google Scholar 

  2. Hsieh J, Liu E, Nett B, Tang J, Thibault J-B, Sahney S (2019) A new era of image reconstruction: TrueFidelity™. White Paper (JB68676XX), GE Healthcare

  3. Koetzier LR, Mastrodicasa D, Szczykutowicz TP et al (2023) Deep learning image reconstruction for CT: technical principles and clinical prospects. Radiology 306:e221257. https://doi.org/10.1148/radiol.221257

    Article  PubMed  Google Scholar 

  4. Wong KK, Cummock JS, He Y, Ghosh R, Volpi JJ, Wong S (2021) Retrospective study of deep learning to reduce noise in non-contrast head CT images. Comput Med Imaging Graph 94:101996. https://doi.org/10.1016/j.compmedimag.2021.101996

    Article  PubMed  Google Scholar 

  5. Jiang B, Li N, Shi X et al (2022) Deep learning reconstruction shows better lung nodule detection for ultra-low-dose chest CT. Radiology 303:202–212. https://doi.org/10.1148/radiol.210551

    Article  PubMed  Google Scholar 

  6. 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. https://doi.org/10.1016/j.jcct.2020.01.002

    Article  PubMed  Google Scholar 

  7. Matsukiyo R, Ohno Y, Matsuyama T et al (2020) Deep learning-based and hybrid-type iterative reconstructions for CT: comparison of capability for quantitative and qualitative image quality improvements and small vessel evaluation at dynamic CE-abdominal CT with ultra-high and standard resolutions. Jpn J Radiol 39:186–197. https://doi.org/10.1007/s11604-020-01045-w

    Article  PubMed  Google Scholar 

  8. Jensen CT, Gupta S, Saleh MM et al (2022) Reduced-dose deep learning reconstruction for abdominal CT of liver metastases. Radiology 303:90–98. https://doi.org/10.1148/radiol.211838

    Article  PubMed  Google Scholar 

  9. Lyu P, Liu N, Harrawood B et al (2023) Is it possible to use low-dose deep learning reconstruction for the detection of liver metastases on CT routinely. Eur Radiol 33:1629–1640. https://doi.org/10.1007/s00330-022-09206-3

    Article  CAS  PubMed  Google Scholar 

  10. Koike Y, Ohira S, Teraoka Y et al (2022) Pseudo low-energy monochromatic imaging of head and neck cancers: deep learning image reconstruction with dual-energy CT. Int J Comput Assist Radiol Surg 17:1271–1279. https://doi.org/10.1007/s11548-022-02627-x

    Article  PubMed  Google Scholar 

  11. Noda Y, Kawai N, Kawamura T et al (2022) Radiation and iodine dose reduced thoraco-abdomino-pelvic dual-energy CT at 40 keV reconstructed with deep learning image reconstruction. Br J Radiol 95:20211163 https://doi.org/10.1259/bjr.20211163

  12. Noda Y, Kawai N, Nagata S et al (2022) Deep learning image reconstruction algorithm for pancreatic protocol dual-energy computed tomography: image quality and quantification of iodine concentration. Eur Radiol 32:384–394. https://doi.org/10.1007/s00330-021-08121-3

    Article  CAS  PubMed  Google Scholar 

  13. Lennartz S, Hokamp NG, Kambadakone A (2022) Dual-energy CT of the abdomen: radiology in training. Radiology 305:19–27. https://doi.org/10.1148/radiol.212914

    Article  PubMed  Google Scholar 

  14. Lv P, Zhou Z, Liu J et al (2019) Can virtual monochromatic images from dual-energy CT replace low-kVp images for abdominal contrast-enhanced CT in small- and medium-sized patients. Eur Radiol 29:2878–2889. https://doi.org/10.1007/s00330-018-5850-z

    Article  PubMed  Google Scholar 

  15. Lee T, Lee JM, Yoon JH et al (2022) Deep learning-based image reconstruction of 40-keV virtual monoenergetic images of dual-energy CT for the assessment of hypoenhancing hepatic metastasis. Eur Radiol 32:6407–6417. https://doi.org/10.1007/s00330-022-08728-0

    Article  CAS  PubMed  Google Scholar 

  16. Sato M, Ichikawa Y, Domae K et al (2022) Deep learning image reconstruction for improving image quality of contrast-enhanced dual-energy CT in abdomen. Eur Radiol 32:5499–5507. https://doi.org/10.1007/s00330-022-08647-0

    Article  PubMed  Google Scholar 

  17. Kanal KM, Butler PF, Sengupta D, Bhargavan-Chatfield M, Coombs LP, Morin RL (2017) U.S. diagnostic reference levels and achievable doses for 10 adult CT examinations. Radiology 284:120–133. https://doi.org/10.1148/radiol.2017161911

    Article  PubMed  Google Scholar 

  18. Jensen CT, Liu X, Tamm EP et al (2020) Image quality assessment of abdominal CT by use of new deep learning image reconstruction: initial experience. AJR Am J Roentgenol 215:50–57. https://doi.org/10.2214/AJR.19.22332

    Article  PubMed  Google Scholar 

  19. Park S, Yoon JH, Joo I et al (2022) Image quality in liver CT: low-dose deep learning vs standard-dose model-based iterative reconstructions. Eur Radiol 32:2865–2874. https://doi.org/10.1007/s00330-021-08380-0

    Article  CAS  PubMed  Google Scholar 

  20. Ichikawa T, Erturk SM, Araki T (2006) Multiphasic contrast-enhanced multidetector-row CT of liver: contrast-enhancement theory and practical scan protocol with a combination of fixed injection duration and patients' body-weight-tailored dose of contrast material. Eur J Radiol 58:165–176. https://doi.org/10.1016/j.ejrad.2005.11.037

    Article  PubMed  Google Scholar 

  21. Lv P, Liu J, Chai Y, Yan X, Gao J, Dong J (2017) Automatic spectral imaging protocol selection and iterative reconstruction in abdominal CT with reduced contrast agent dose: initial experience. Eur Radiol 27:374–383. https://doi.org/10.1007/s00330-016-4349-8

    Article  PubMed  Google Scholar 

  22. Marin D, Nelson RC, Schindera ST et al (2010) Low-tube-voltage, high-tube-current multidetector abdominal CT: improved image quality and decreased radiation dose with adaptive statistical iterative reconstruction algorithm--initial clinical experience. Radiology 254:145–153. https://doi.org/10.1148/radiol.09090094

    Article  PubMed  Google Scholar 

  23. Son W, Kim M, Hwang JY et al (2022) Comparison of a deep learning-based reconstruction algorithm with filtered back projection and iterative reconstruction algorithms for pediatric abdominopelvic CT. Korean J Radiol 23:752–762

    Article  PubMed  PubMed Central  Google Scholar 

  24. Greffier J, Durand Q, Frandon J et al (2022) Improved image quality and dose reduction in abdominal CT with deep-learning reconstruction algorithm: a phantom study. Eur Radiol 33:699–710. https://doi.org/10.1007/s00330-021-08459-8

    Article  CAS  PubMed  Google Scholar 

  25. 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. https://doi.org/10.1007/s00330-020-06724-w

    Article  PubMed  Google Scholar 

  26. Cester D, Eberhard M, Alkadhi H, Euler A (2022) Virtual monoenergetic images from dual-energy CT: systematic assessment of task-based image quality performance. Quant Imaging Med Surg 12:726–741. https://doi.org/10.21037/qims-21-477

    Article  PubMed  PubMed Central  Google Scholar 

  27. Jensen CT, Wagner-Bartak NA, Vu LN et al (2019) Detection of colorectal hepatic metastases is superior at standard radiation dose CT versus reduced dose CT. Radiology 290:400–409. https://doi.org/10.1148/radiol.2018181657

    Article  PubMed  Google Scholar 

  28. Boone JMSK, Cody DD, McCollough CH, McNitt-Gray MF, Toth TL (2011) Size-specific dose estimates (SSDE) in pediatric and adult body CT examinations. In: Report of Am Assoc Phys Med AAPM Task Group 204. American Association of Physicists in Medicine, College Park

  29. Landis JR, Koch GG (1977) The measurement of observer agreement for categorical data. Biometrics 33:159–174 PMID: 843571

    Article  CAS  PubMed  Google Scholar 

  30. Xu JJ, Lönn L, Budtz-Jørgensen E, Hansen KL, Ulriksen PS (2022) Quantitative and qualitative assessments of deep learning image reconstruction in low-keV virtual monoenergetic dual-energy CT. Eur Radiol 32:7098–7107. https://doi.org/10.1007/s00330-022-09018-5

    Article  CAS  PubMed  Google Scholar 

  31. 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. https://doi.org/10.1002/mp.14319

    Article  PubMed  Google Scholar 

  32. Masuda S, Yamada Y, Minamishima K, Owaki Y, Yamazaki A, Jinzaki M (2022) Impact of noise reduction on radiation dose reduction potential of virtual monochromatic spectral images: comparison of phantom images with conventional 120 kVp images using deep learning image reconstruction and hybrid iterative reconstruction. Eur J Radiol 149:110198. https://doi.org/10.1016/j.ejrad.2022.110198

    Article  PubMed  Google Scholar 

  33. Noda Y, Kaga T, Kawai N et al (2021) Low-dose whole-body CT using deep learning image reconstruction: image quality and lesion detection. Br J Radiol 94:20201329. https://doi.org/10.1259/bjr.20201329

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Funding

Dr. Peijie Lyu received financial support from Key Scientific Research Project of Higher Education in Henan Province (22A320057).

Author information

Author notes

  1. Peijie Lyu and Zhen Li have contributed equally to this work and share first authorship.

Authors and Affiliations

  1. Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan Province, China

    Peijie Lyu, Yan Chen, Huixia Wang, Nana Liu, Jie Liu, Pengchao Zhan, Xing Liu, Bo Shang & Jianbo Gao

  2. Department of Interventional Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan Province, China

    Zhen Li

  3. CT Imaging Research Center, GE Healthcare China, Beijing, China

    Luotong Wang

Authors
  1. Peijie Lyu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zhen Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yan Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Huixia Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Nana Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Jie Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Pengchao Zhan
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Xing Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Bo Shang
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Luotong Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Jianbo Gao
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jianbo Gao.

Ethics declarations

Guarantor

The scientific guarantor of this publication is Dr. Jianbo Gao.

Conflict of interest

The authors of this manuscript declare relationships with the following companies: GE Healthcare China for Luotong Wang. The other authors declare that they have no conflict of interest.

Statistics and biometry

No complex statistical methods were necessary for this paper.

Informed consent

The institutional review board approved this single-institution prospective study (Registry number: ChiCTR-DPD-16010302), and all participants provided written informed consent before enrollment.

Ethical approval

Institutional Review Board approval was obtained (The First Affiliated Hospital of Zhengzhou University).

Study subjects or cohorts overlap

No study subjects or cohorts have been previously reported.

Methodology

  • Prospective

  • Diagnostic 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.

Supplementary Information

ESM 1

(PDF 345 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

Lyu, P., Li, Z., Chen, Y. et al. Deep learning reconstruction CT for liver metastases: low-dose dual-energy vs standard-dose single-energy. Eur Radiol 34, 28–38 (2024). https://doi.org/10.1007/s00330-023-10033-3

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00330-023-10033-3

Keywords

Search

Navigation