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Noise reduction approach in pediatric abdominal CT combining deep learning and dual-energy technique

European Radiology volume 31, pages 2218–2226 (2021)Cite this article

Abstract

Objectives

To evaluate the image quality of low iodine concentration, dual-energy CT (DECT) combined with a deep learning–based noise reduction technique for pediatric abdominal CT, compared with standard iodine concentration single-energy polychromatic CT (SECT).

Methods

From December 2016 to May 2017, DECT with 300 mg•I/mL contrast medium was performed in 29 pediatric patients (17 boys, 12 girls; age, 2–19 years). The DECT images were reconstructed using a noise-optimized virtual monoenergetic reconstruction image (VMI) with and without a deep learning method. SECT images with 350 mg•I/mL contrast medium, performed within the last 3 months before the DECT, served as reference images. The quantitative and qualitative parameters were compared using paired t tests and Wilcoxon signed-rank tests, and the differences in radiation dose and total iodine administration were assessed.

Results

The linearly blended DECT showed lower attenuation and higher noise than SECT. The 60-keV VMI showed an increase in attenuation and higher noise than SECT. The combined 60-keV VMI plus deep learning images showed low noise, no difference in contrast-to-noise ratios, and overall image quality or diagnostic image quality, but showed a higher signal-to-noise ratio in the liver and lower enhancement of lesions than SECT. The overall image and diagnostic quality of lesions were maintained on the combined noise reduction approach. The CT dose index volume and total iodine administration in DECT were respectively 19.6% and 14.3% lower than those in SECT.

Conclusion

Low iodine concentration DECT, combined with deep learning in pediatric abdominal CT, can maintain image quality while reducing the radiation dose and iodine load, compared with standard SECT.

Key Points

• An image noise reduction approach combining deep learning and noise-optimized virtual monoenergetic image reconstruction can maintain image quality while reducing radiation dose and iodine load.

• The 60-keV virtual monoenergetic image reconstruction plus deep learning images showed low noise, no difference in contrast-to-noise ratio, and overall image quality, but showed a higher signal-to-noise ratio in the liver and a lower enhancement of lesion than single-energy polychromatic CT.

• This combination could offer a 19.6% reduction in radiation dose and a 14.3% reduction in iodine load, in comparison with a control group that underwent single-energy polychromatic CT with the standard protocol.

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Abbreviations

CNR:

Contrast-to-noise ratio

CT:

Computed tomography

CTDIvol :

Computed tomography dose index volume

DECT:

Dual-energy computed tomography

DL:

deep learning

DLP:

Dose-length product

ED:

Effective dose

HU:

Hounsfield unit

keV:

Kiloelectron volt

SECT:

Single-energy polychromatic computed tomography

SNR:

Signal-to-noise ratio

VMI:

Virtual monoenergetic reconstruction image

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Funding

This study was supported by a research fund from the TAEJOON PHARM Co., Ltd, Korea (No. 06-2017-0010 / TJC-1603-501).

Author information

Authors and Affiliations

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

    Seunghyun Lee, Young Hun Choi, Yeon Jin Cho, Seul Bi Lee, Jung-Eun Cheon & Woo Sun Kim

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

    Young Hun Choi, Jung-Eun Cheon, Woo Sun Kim & Jong Hyo Kim

  3. Institute of Radiation Medicine, Seoul National University Medical Research Center, 103 Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea

    Jung-Eun Cheon & Woo Sun Kim

  4. Department of Transdisciplinary Studies, Graduate School of Convergence Science and Technology, Seoul National University, 103 Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea

    Chul Kyun Ahn & Jong Hyo Kim

  5. Advanced Institutes of Convergence Technology, Seoul National University, 103 Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea

    Jong Hyo Kim

Authors
  1. Seunghyun Lee
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Corresponding author

Correspondence to Young Hun Choi.

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Guarantor

The scientific guarantor of this publication is Young Hun Choi.

Conflict of interest

This study was supported by a research fund from the TAEJOON PHARM Co., Ltd, Korea (No. 06-2017-0010 / TJC-1603-501) whose products may be related to the subject matter of the article.

Statistics and biometry

The authors (Seunghyun Lee and Young Hun Choi) have significant statistical expertise.

No complex statistical methods were necessary for this paper.

Informed consent

Written informed consent was waived by the Institutional Review Board.

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Institutional Review Board approval was obtained.

Methodology

• retrospective

• diagnostic study

• performed at one institution

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Lee, S., Choi, Y.H., Cho, Y.J. et al. Noise reduction approach in pediatric abdominal CT combining deep learning and dual-energy technique. Eur Radiol 31, 2218–2226 (2021). https://doi.org/10.1007/s00330-020-07349-9

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  • DOI: https://doi.org/10.1007/s00330-020-07349-9

Keywords

  • Child
  • Tomography, X-ray computed
  • Deep learning
  • Radiation dosage
  • Iodine