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Deep learning–based image reconstruction of 40-keV virtual monoenergetic images of dual-energy CT for the assessment of hypoenhancing hepatic metastasis

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

Abstract

Objectives

To evaluate the diagnostic value of deep learning model (DLM) reconstructed dual-energy CT (DECT) low-keV virtual monoenergetic imaging (VMI) for assessing hypoenhancing hepatic metastases.

Methods

This retrospective study included 131 patients who underwent contrast-enhanced DECT (80-kVp and 150-kVp with a tin filter) in the portal venous phase for hepatic metastasis surveillance. Linearly blended images simulating 100-kVp images (100-kVp), standard 40-keV VMI images (40-keV VMI), and post-processed 40-keV VMI using a vendor-agnostic DLM (i.e., DLM 40-keV VMI) were reconstructed. Lesion conspicuity and diagnostic acceptability were assessed by three independent reviewers and compared using the Wilcoxon signed-rank test. The contrast-to-noise ratios (CNRs) were also measured placing ROIs in metastatic lesions and liver parenchyma. The detection performance of hepatic metastases was assessed by using a jackknife alternative free-response ROC method. The consensus by two independent radiologists was used as the reference standard.

Results

DLM 40-keV VMI, compared to 40-keV VMI and 100-kVp, showed a higher lesion-to-liver CNR (8.25 ± 3.23 vs. 6.05 ± 2.38 vs. 5.99 ± 2.00), better lesion conspicuity (4.3 (4.0–4.7) vs. 3.7 (3.7–4.0) vs. 3.7 (3.3–4.0)), and better diagnostic acceptability (4.3 (4.0–4.3) vs. 3.0 (2.7–3.3) vs. 4.0 (4.0–4.3)) (p < 0.001 for all). For lesion detection (246 hepatic metastases in 68 patients), the figure of merit was significantly higher with DLM 40-keV VMI than with 40-keV VMI (0.852 vs. 0.822, p = 0.012), whereas no significant difference existed between DLM 40-keV VMI and 100-kVp (0.852 vs. 0.842, p = 0.31).

Conclusions

DLM 40-keV VMI provided better image quality and comparable diagnostic performance for detecting hypoenhancing hepatic metastases compared to linearly blended images.

Key Points

• DLM 40-keV VMI provides a superior image quality compared with 40-keV or 100-kVp for assessing hypoenhancing hepatic metastasis.

• DLM 40-keV VMI has the highest CNR and lesion conspicuity score for hypoenhancing hepatic metastasis due to noise reduction and structural preservation.

• DLM 40-keV VMI provides higher lesion detectability than standard 40-keV VMI (p = 0.012).

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Abbreviations

ADMIRE:

Advanced modeled iterative reconstruction

CNR:

Contrast-to-noise ratio

DLM:

Deep learning model

ERS:

Edge rise slope

FOM:

Figure of merit

JAFROC:

Jackknife alternative free-response receiver operating characteristic

SNR:

Signal-to-noise ratio

VMI:

Virtual monoenergetic imaging

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Acknowledgements

This work was supported by the Korea Medical Device Development Fund grant funded by the Korea 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) (Project Number: KMDF_PR_20200901_0226, 9991006891).

Funding

The authors state that this work has not received any funding.

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Authors and Affiliations

  1. Department of Radiology, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Korea

    Taehee Lee, Jeong Min Lee, Jeong Hee Yoon, Ijin Joo, Jae Seok Bae, Jeongin Yoo & Jae Hyun Kim

  2. Department of Radiology, Seoul National University College of Medicine, 103 Daehak-ro, Jongno-gu, Seoul, 03080, Korea

    Jeong Min Lee, Jeong Hee Yoon, Ijin Joo & Jong Hyo Kim

  3. Institute of Radiation Medicine, Seoul National University Medical Research Center, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Korea

    Jeong Min Lee

  4. Department of Transdisciplinary Studies, Graduate School of Convergence Science and Technology, Seoul National University, Seoul, 08826, Korea

    Chulkyun Ahn & Jong Hyo Kim

  5. Center for Medical-IT Convergence Technology Research, Advanced Institutes of Convergence Technology, Suwon, 16229, Korea

    Jong Hyo Kim

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  1. Taehee Lee
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Correspondence to Jeong Min Lee.

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The scientific guarantor of this publication is Jeong Min Lee.

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One author (J.H.K.) is CO-CEO & CTO of ClariPI, but did not have control over any of the data or information submitted for publication. The 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.

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Lee, T., Lee, J.M., Yoon, J.H. et al. 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 (2022). https://doi.org/10.1007/s00330-022-08728-0

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