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Deep learning image reconstruction algorithm for pancreatic protocol dual-energy computed tomography: image quality and quantification of iodine concentration

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

Abstract

Objectives

To evaluate the image quality and iodine concentration (IC) measurements in pancreatic protocol dual-energy computed tomography (DECT) reconstructed using deep learning image reconstruction (DLIR) and compare them with those of images reconstructed using hybrid iterative reconstruction (IR).

Methods

The local institutional review board approved this prospective study. Written informed consent was obtained from all participants. Thirty consecutive participants with pancreatic cancer (PC) underwent pancreatic protocol DECT for initial evaluation. DECT data were reconstructed at 70 keV using 40% adaptive statistical iterative reconstruction–Veo (hybrid-IR) and DLIR at medium and high levels (DLIR-M and DLIR-H, respectively). The diagnostic acceptability and conspicuity of PC were qualitatively assessed using a 5-point scale. IC values of the abdominal aorta, pancreas, PC, liver, and portal vein; standard deviation (SD); and coefficient of variation (CV) were calculated. Qualitative and quantitative parameters were compared between the hybrid-IR, DLIR-M, and DLIR-H groups.

Results

The diagnostic acceptability and conspicuity of PC were significantly better in the DLIR-M group compared with those in the other groups (p < .001–.001). The IC values of the anatomical structures were almost comparable between the three groups (p = .001–.9). The SD of IC values was significantly lower in the DLIR-H group (p < .001) and resulted in the lowest CV (p < .001–.002) compared with those in the hybrid-IR and DLIR-M groups.

Conclusions

DLIR could significantly improve image quality and reduce the variability of IC values than could hybrid-IR.

Key Points

  • Image quality and conspicuity of pancreatic cancer were the best in DLIR-M.

  • DLIR significantly reduced background noise and improved SNR and CNR.

  • The variability of iodine concentration was reduced in DLIR.

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Abbreviations

CNR:

Contrast-to-noise ratio

CT:

Computed tomography

CV:

Coefficient of variation

DECT:

Dual-energy CT

DLIR:

Deep learning image reconstruction

IC:

Iodine concentration

IR:

Iterative reconstruction

PC:

Pancreatic cancer

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.

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Authors and Affiliations

  1. Department of Radiology, Gifu University, 1-1 Yanagido, Gifu, 501-1194, Japan

    Yoshifumi Noda, Nobuyuki Kawai, Shoma Nagata, Fumihiko Nakamura, Takayuki Mori, Hiroki Kato & Masayuki Matsuo

  2. Department of Radiology Services, Gifu University Hospital, 1-1 Yanagido, Gifu, 501-1194, Japan

    Toshiharu Miyoshi, Ryosuke Suzuki & Fumiya Kitahara

  3. Department of Radiology, Frontier Science for Imaging, Gifu University, 1-1 Yanagido, Gifu, 501-1194, Japan

    Fuminori Hyodo

Authors
  1. Yoshifumi Noda
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  2. Nobuyuki Kawai
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  3. Shoma Nagata
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  4. Fumihiko Nakamura
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  5. Takayuki Mori
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  6. Toshiharu Miyoshi
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  7. Ryosuke Suzuki
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  8. Fumiya Kitahara
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  9. Hiroki Kato
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  10. Fuminori Hyodo
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  11. Masayuki Matsuo
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Corresponding author

Correspondence to Yoshifumi Noda.

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Guarantor

The scientific guarantor of this publication is Yoshifumi Noda.

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 not required for this study because this is retrospective study.

Ethical approval

Institutional Review Board approval was obtained.

Methodology

  • prospective

  • diagnostic or prognostic study

  • performed at one institution

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

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  • DOI: https://doi.org/10.1007/s00330-021-08121-3

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