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Deep learning image reconstruction for improving image quality of contrast-enhanced dual-energy CT in abdomen

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

Abstract

Objectives

To evaluate the usefulness of deep learning image reconstruction (DLIR) to improve the image quality of dual-energy computed tomography (DECT) of the abdomen, compared to hybrid iterative reconstruction (IR).

Methods

This study included 40 patients who underwent contrast-enhanced DECT of the abdomen. Virtual monochromatic 40-, 50-, and 70-keV and iodine density images were reconstructed using three reconstruction algorithms, including hybrid IR (ASiR-V50%) and DLIR (TrueFidelity) at medium- and high-strength level (DLIR-M and DLIR-H, respectively). The standard deviation of attenuation in liver parenchyma was measured as image noise. The contrast-to-noise ratio (CNR) for the portal vein on portal venous phase CT was calculated. The vessel conspicuity and overall image quality were graded on a 5-point scale ranging from 1 (poor) to 5 (excellent). The comparative scale of lesion conspicuity in 47 abdominal solid lesions was evaluated on a 5-point scale ranging from 0 (best) to −4 (markedly inferior).

Results

The image noise of virtual monochromatic 40-, 50 -, and 70-keV and iodine density images was significantly decreased by DLIR compared to hybrid IR (p < 0.0001). The CNR was significantly higher in DLIR-H and DLIR-M than in hybrid IR (p < 0.0001). The vessel conspicuity and overall image quality scores were also significantly greater in DLIR-H and DLIR-M than in hybrid IR (p < 0.05). The lesion conspicuity scores for DLIR-M and DLIR-H were significantly higher than those for hybrid IR in the virtual monochromatic image of all energy levels (p ≤ 0.001).

Conclusions

DLIR improves vessel conspicuity, CNR, and lesion conspicuity of virtual monochromatic and iodine density images in abdominal contrast-enhanced DECT, compared to hybrid IR.

Key Points

• Deep learning image reconstruction (DLIR) is useful for reducing image noise and improving the CNR of visual monochromatic 40-, 50-, and 70-keV images in dual-energy CT.

• DLIR can improve lesion conspicuity of abdominal solid lesions on virtual monochromatic images compared to hybrid iterative reconstruction.

• DLIR can also be applied to iodine density maps and significantly improves their image quality.

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Abbreviations

CNR:

Contrast-to-noise ratio

CT:

Computed tomography

CTDIvol:

Computed tomography dose index

DCNN:

Deep convolutional neural networks

DECT:

Dual-energy computed tomography

DLIR:

Deep learning image reconstruction

DLIR-H:

Deep learning image reconstruction at high-strength level

DLIR-M:

Deep learning image reconstruction at medium-strength level

DLP:

Dose-length product

IQR:

Interquartile range

IR:

Iterative reconstruction

MBIR:

Model-based iterative reconstruction

ROIs:

Regions of interest

SD:

Standard deviation

SNR:

Signal-to-noise ratio

VMI:

Virtual monochromatic image

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Funding

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

Author information

Authors and Affiliations

  1. Department of Radiology, Mie University Hospital, 2-174 Edobashi, Tsu, Mie, 514-8507, Japan

    Mineka Sato, Yasutaka Ichikawa, Kensuke Domae, Kazuya Yoshikawa, Yoshinori Kanii, Akio Yamazaki, Naoki Nagasawa, Motonori Nagata, Masaki Ishida & Hajime Sakuma

Authors
  1. Mineka Sato
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  2. Yasutaka Ichikawa
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  3. Kensuke Domae
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  4. Kazuya Yoshikawa
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  5. Yoshinori Kanii
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  6. Akio Yamazaki
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  7. Naoki Nagasawa
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  8. Motonori Nagata
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  9. Masaki Ishida
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  10. Hajime Sakuma
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Corresponding author

Correspondence to Yasutaka Ichikawa.

Ethics declarations

The authors declare that this study was conducted according to the principles of the Declaration of Helsinki.

Guarantor

The scientific guarantor of this publication is Dr. Hajime Sakuma (professor, Department of Radiology, Mie University Hospital, Japan).

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

No complex statistical methods were necessary for this paper.

Informed consent

Written informed consent was waived by the Institutional Review Board since this study used existing clinical CT image data. The opportunity to opt out of the inclusion to this study was given through a notice in the hospital website. No patient showed intention for an exclusion from this study.

Ethical approval

This study was approved by the Institutional Review Board of Mie University Hospital (Approval number: H2019-207).

Methodology

• retrospective study

• performed at a single center

• diagnostic or prognostic study

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

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  • DOI: https://doi.org/10.1007/s00330-022-08647-0

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