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Improving lesion conspicuity in abdominal dual-energy CT with deep learning image reconstruction: a prospective study with five readers

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

Abstract

Objectives

To evaluate image quality, diagnostic acceptability, and lesion conspicuity in abdominal dual-energy CT (DECT) using deep learning image reconstruction (DLIR) compared to those using adaptive statistical iterative reconstruction-V (Asir-V) at 50% blending (AV-50), and to identify potential factors impacting lesion conspicuity.

Methods

The portal-venous phase scans in abdominal DECT of 47 participants with 84 lesions were prospectively included. The raw data were reconstructed to virtual monoenergetic image (VMI) at 50 keV using filtered back-projection (FBP), AV-50, and DLIR at low (DLIR-L), medium (DLIR-M), and high strength (DLIR-H). A noise power spectrum (NPS) was generated. CT number and standard deviation values of eight anatomical sites were measured. Signal-to-noise (SNR), and contrast-to-noise ratio (CNR) values were calculated. Five radiologists assessed image quality in terms of image contrast, image noise, image sharpness, artificial sensation, and diagnostic acceptability, and evaluated the lesion conspicuity.

Results

DLIR further reduced image noise (p < 0.001) compared to AV-50 while better preserved the average NPS frequency (p < 0.001). DLIR maintained CT number values (p > 0.99) and improved SNR and CNR values compared to AV-50 (p < 0.001). DLIR-H and DLIR-M showed higher ratings in all image quality analyses than AV-50 (p < 0.001). DLIR-H provided significantly better lesion conspicuity than AV-50 and DLIR-M regardless of lesion size, relative CT attenuation to surrounding tissue, or clinical purpose (p < 0.05).

Conclusions

DLIR-H could be safely recommended for routine low-keV VMI reconstruction in daily contrast-enhanced abdominal DECT to improve image quality, diagnostic acceptability, and lesion conspicuity.

Key Points

• DLIR is superior to AV-50 in noise reduction, with less shifts of the average spatial frequency of NPS towards low frequency, and larger improvements of NPS noise, noise peak, SNR, and CNR values.

• DLIR-M and DLIR-H generate better image quality in terms of image contrast, noise, sharpness, artificial sensation, and diagnostic acceptability than AV-50, while DLIR-H provides better lesion conspicuity than AV-50 and DLIR-M.

• DLIR-H could be safely recommended as a new standard for routine low-keV VMI reconstruction in contrast-enhanced abdominal DECT to provide better lesion conspicuity and better image quality than the standard AV-50.

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Abbreviations

Asir-V:

Adaptive statistical iterative reconstruction-V

CNR:

Contrast-to-noise ratio

DECT:

Dual-energy computed tomography

DLIR:

Deep learning image reconstruction

faverage :

Average spatial frequency of noise power spectrum

FBP:

Filtered back-projection

fpeak :

Peak spatial frequency of noise power spectrum

HU:

Hounsfield unit

keV:

Kiloelectron volt

NPS:

Noise power spectrum

ROI:

Region of interest

SD:

Standard deviation

SNR:

Signal-to-noise ratio

VMI:

Virtual monoenergetic images

References

  1. Albrecht MH, Vogl TJ, Martin SS et al (2019) Review of clinical applications for virtual monoenergetic dual-energy CT. Radiology 293(2):260–271. https://doi.org/10.1148/radiol.2019182297

    Article  PubMed  Google Scholar 

  2. D’Angelo T, Cicero G, Mazziotti S et al (2019) Dual energy computed tomography virtual monoenergetic imaging: technique and clinical applications. Br J Radiol 92(1098):20180546. https://doi.org/10.1259/bjr.20180546

    Article  PubMed  PubMed Central  Google Scholar 

  3. Leng S, Yu L, Fletcher JG, McCollough CH (2015) Maximizing iodine contrast-to-noise ratios in abdominal CT imaging through use of energy domain noise reduction and virtual monoenergetic dual-energy CT. Radiology 276:562–570. https://doi.org/10.1148/radiol.2015140857

    Article  PubMed  Google Scholar 

  4. 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. https://doi.org/10.1097/RLI.0000000000000272

    Article  CAS  PubMed  Google Scholar 

  5. Greffier J, Frandon J, Hamard A et al (2020) Impact of iterative reconstructions on image quality and detectability of focal liver lesions in low-energy monochromatic images. Phys Med 77:36–42. https://doi.org/10.1016/j.ejmp.2020.07.024

    Article  CAS  PubMed  Google Scholar 

  6. Greffier J, Viry A, Barbotteau Y et al (2022) Phantom task-based image quality assessment of three generations of rapid kV-switching dual-energy CT systems on virtual monoenergetic images. Med Phys 49(4):2233–2244. https://doi.org/10.1002/mp.15558

    Article  PubMed  Google Scholar 

  7. 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

  8. 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(1):384–394. https://doi.org/10.1007/s00330-021-08121-3

    Article  CAS  PubMed  Google Scholar 

  9. 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(8):5499–5507. https://doi.org/10.1007/s00330-022-08647-0

    Article  PubMed  Google Scholar 

  10. Noda Y, Nakamura F, Kawamura T et al (2021) Deep-learning image-reconstruction algorithm for dual-energy CT angiography with reduced iodine dose: preliminary results. Clin Radiol 77(2):e138–e146. https://doi.org/10.1016/j.crad.2021.10.014

    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(1134):20211163. https://doi.org/10.1259/bjr.20211163

    Article  PubMed  Google Scholar 

  12. Fair E, Profio M, Kulkarni N et al (2022) Image quality evaluation in dual-energy CT of the chest, abdomen, and pelvis in obese patients with deep learning image reconstruction. J Comput Assist Tomogr 46(4):604–611. https://doi.org/10.1097/RCT.0000000000001316

    Article  PubMed  Google Scholar 

  13. 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(10):7098–7107. https://doi.org/10.1007/s00330-022-09018-5

    Article  CAS  PubMed  Google Scholar 

  14. Bie Y, Yang S, Li X, Zhao K, Zhang C, Zhong H (2022) Impact of deep learning-based image reconstruction on image quality compared with adaptive statistical iterative reconstruction-Veo in renal and adrenal computed tomography. J Xray Sci Technol 30(3):409–418. https://doi.org/10.3233/XST-211105

    Article  PubMed  PubMed Central  Google Scholar 

  15. Samei E, Bakalyar D, Boedeker KL et al (2019) Performance evaluation of computed tomography systems: summary of AAPM task group 233. Med Phys 46(11):e735–e756. https://doi.org/10.1002/mp.13763

    Article  PubMed  Google Scholar 

  16. Brady SL, Trout AT, Somasundaram E, Anton CG, Li Y, Dillman JR (2021) Improving image quality and reducing radiation dose for pediatric CT by using deep learning reconstruction. Radiology 298(1):180–188. https://doi.org/10.1148/radiol.2020202317

    Article  PubMed  Google Scholar 

  17. 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(7):752–762. https://doi.org/10.3348/kjr.2021.0466

    Article  PubMed  PubMed Central  Google Scholar 

  18. 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(5):2865–2874. https://doi.org/10.1007/s00330-021-08380-0

    Article  CAS  PubMed  Google Scholar 

  19. 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(5):3156–3164. https://doi.org/10.1007/s00330-020-07358-8

    Article  PubMed  Google Scholar 

  20. Jensen CT, Gupta S, Saleh MM et al (2022) Reduced-dose deep learning reconstruction for abdominal CT of liver metastases. Radiology 303(1):90–98. https://doi.org/10.1148/radiol.211838

    Article  PubMed  Google Scholar 

  21. Mangiafico SS (2016) Summary and analysis of extension program evaluation in R, version 1.19.10. Accessed via rcompanion.org/handbook/ on Oct 2022

  22. Koo TK, Li MY (2016) A guideline of selecting and reporting intraclass correlation coefficients for reliability research. J Chiropr Med 15(2):155–163. https://doi.org/10.1016/j.jcm.2016.02.012

    Article  PubMed  PubMed Central  Google Scholar 

  23. Gisev N, Bell JS, Chen TF (2013) Interrater agreement and interrater reliability: key concepts, approaches, and applications. Res Social Adm Pharm 9(3):330–338. https://doi.org/10.1016/j.sapharm.2012.04.004

    Article  PubMed  Google Scholar 

  24. Serdar CC, Cihan M, Yücel D, Serdar MA (2021) Sample size, power and effect size revisited: simplified and practical approaches in pre-clinical, clinical and laboratory studies. Biochem Med (Zagreb) 31(1):010502. https://doi.org/10.11613/BM.2021.010502

  25. van Stiphout JA, Driessen J, Koetzier LR et al (2022) The effect of deep learning reconstruction on abdominal CT densitometry and image quality: a systematic review and meta-analysis. Eur Radiol 32(5):2921–2929. https://doi.org/10.1007/s00330-021-08438-z

    Article  PubMed  Google Scholar 

  26. Chen Y, Zhong J, Wang L et al (2022) Robustness of CT radiomics features: consistency within and between single-energy CT and dual-energy CT. Eur Radiol 32(8):5480–5490. https://doi.org/10.1007/s00330-022-08628-3

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Zhong J, Xia Y, Chen Y et al (2023) Deep learning image reconstruction algorithm reduces image noise while alters radiomics features in dual-energy CT in comparison with conventional iterative reconstruction algorithms: a phantom study. Eur Radiol 33(2):812–824. https://doi.org/10.1007/s00330-022-09119-1

    Article  CAS  PubMed  Google Scholar 

  28. Cao L, Liu X, Qu T et al (2023) Improving spatial resolution and diagnostic confidence with thinner slice and deep learning image reconstruction in contrast-enhanced abdominal CT. Eur Radiol 33(3):1603–1611. https://doi.org/10.1007/s00330-022-09146-y

    Article  CAS  PubMed  Google Scholar 

  29. Chen Y, Zhong J, Wang L et al (2022) Multivendor comparison of quantification accuracy of iodine concentration and attenuation measurements by dual-energy CT: a phantom study. AJR Am J Roentgenol 219(5):827–839. https://doi.org/10.2214/AJR.22.27753

    Article  PubMed  Google Scholar 

  30. Fukutomi A, Sofue K, Ueshima E et al (2023) Deep learning image reconstruction to improve accuracy of iodine quantification and image quality in dual-energy CT of the abdomen: a phantom and clinical study. Eur Radiol 33(2):1388–1399. https://doi.org/10.1007/s00330-022-09127-1

    Article  CAS  PubMed  Google Scholar 

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Acknowledgements

The authors would like to express their gratitude to Dr. Zhen Pan for her assistance in image quality assessment, and Dr. Shiqi Mao for his advice on data visualization.

Funding

This study has received funding from the National Natural Science Foundation of China (82271934, 82101986), the Yangfan Project of Science and Technology Commission of Shanghai Municipality (22YF1442400, 20YF1427200), Shanghai Science and Technology Commission Science and Technology Innovation Action Clinical Innovation Field (18411953000), Medicine and Engineering Combination Project of Shanghai Jiao Tong University (YG2019ZDB09, YG2021QN08), Research Fund of Tongren Hospital, Shanghai Jiao Tong University School of Medicine (TRKYRC-XX202204, TRGG202101, TRYJ2021JC06, 2020TRYJ(LB)06, 2020TRYJ(JC)07), and Guangci Innovative Technology Launch Plan of Ruijin Hospital, Shanghai Jiao Tong University School of Medicine (2022-13).

Author information

Author notes

  1. Jingyu Zhong, Lingyun Wang, and Hailin Shen contributed equally to this work and shared co-first authorship.

Authors and Affiliations

  1. Department of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200336, China

    Jingyu Zhong, Yue Xing, Yangfan Hu, Xiang Ge, Defang Ding & Weiwu Yao

  2. Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China

    Lingyun Wang, Fuhua Yan, Lianjun Du & Huan Zhang

  3. Department of Radiology, Suzhou Kowloon Hospital, Shanghai Jiao Tong University School of Medicine, Suzhou, 215028, China

    Hailin Shen

  4. Computed Tomography Research Center, GE Healthcare, Beijing, 100176, China

    Jianying Li

  5. Computed Tomography Research Center, GE Healthcare, Shanghai, 201203, China

    Wei Lu

  6. Department of Materials, Imperial College London, South Kensington Campus, London, SW7 2AZ, UK

    Xiaomeng Shi

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  1. Jingyu Zhong
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Corresponding authors

Correspondence to Lianjun Du, Weiwu Yao or Huan Zhang.

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Guarantor

The scientific guarantor of this publication is Prof. Huan Zhang from the Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine.

Conflict of interest

Dr. Jingyu Zhong acknowledges his position as a member of the Musculoskeletal section of the Scientific Editorial Board of European Radiology. Mr. Wei Lu and Dr. Jianying Li are employees of GE Healthcare. However, they neither had access nor control over the data acquisition and analysis. All other 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

No complex statistical methods were necessary for this paper.

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Written informed consent from all participants was received.

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Institutional Review Board approval was obtained.

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• prospective

• observational study

• performed at one institution

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Huan Zhang, Weiwu Yao, and Lianjun Du are co-corresponding authors.

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Zhong, J., Wang, L., Shen, H. et al. Improving lesion conspicuity in abdominal dual-energy CT with deep learning image reconstruction: a prospective study with five readers. Eur Radiol 33, 5331–5343 (2023). https://doi.org/10.1007/s00330-023-09556-6

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