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Diagnostic value of deep learning reconstruction for radiation dose reduction at abdominal ultra-high-resolution CT

  • Gastrointestinal
  • Published:
European Radiology Aims and scope Submit manuscript

Abstract

Objectives

We evaluated lower dose (LD) hepatic dynamic ultra-high-resolution computed tomography (U-HRCT) images reconstructed with deep learning reconstruction (DLR), hybrid iterative reconstruction (hybrid-IR), or model-based IR (MBIR) in comparison with standard-dose (SD) U-HRCT images reconstructed with hybrid-IR as the reference standard to identify the method that allowed for the greatest radiation dose reduction while preserving the diagnostic value.

Methods

Evaluated were 72 patients who had undergone hepatic dynamic U-HRCT; 36 were scanned with the standard radiation dose (SD group) and 36 with 70% of the SD (lower dose [LD] group). Hepatic arterial and equilibrium phase (HAP, EP) images were reconstructed with hybrid-IR in the SD group, and with hybrid-IR, MBIR, and DLR in the LD group. One radiologist recorded the standard deviation of attenuation in the paraspinal muscle as the image noise. The overall image quality was assessed by 3 other radiologists; they used a 5-point confidence scale ranging from 1 (unacceptable) to 5 (excellent). Superiority and equivalence with prespecified margins were assessed.

Results

With respect to the image noise, in the HAP and EP, LD DLR and LD MBIR images were superior to SD hybrid-IR images; LD hybrid-IR images were neither superior nor equivalent to SD hybrid-IR images. With respect to the quality scores, only LD DLR images were superior to SD hybrid-IR images.

Conclusions

DLR preserved the quality of abdominal U-HRCT images even when scanned with a reduced radiation dose.

Key Points

• Lower dose DLR images were superior to the standard-dose hybrid-IR images quantitatively and qualitatively at abdominal U-HRCT.

• Neither hybrid-IR nor MBIR may allow for a radiation dose reduction at abdominal U-HRCT without compromising the image quality.

• Because DLR allows for a reduction in the radiation dose and maintains the image quality even at the thinnest slice section, DLR should be applied to abdominal U-HRCT scans.

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Abbreviations

AiCE:

Advanced Intelligent Clear-IQ Engine

AIDR3D:

Adaptive Iterative Dose Reduction 3-Dimensional

BMI:

Body mass index

CA:

Chromosomal aberrations

CI:

Confidence interval

CNR:

Contrast-to-noise ratio

CTDIvol :

CT dose index

DICOM:

Digital Imaging and Communications in Medicine

DLP:

Dose-length product

DLR:

Deep learning reconstruction

EP:

Equilibrium phase

FIRST:

Forward projected model based iterative reconstruction solution

HAP:

Hepatic arterial phase

Hybrid-IR:

Hybrid iterative reconstruction

LD:

Lower dose

MBIR:

Model-based iterative reconstruction

ROI:

Region of interest

SD:

Standard dose

SSDE:

Size-specific dose estimate

U-HRCT:

Ultra-high-resolution computed tomography

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Funding

Dr. Kazuo Awai received a research funding from Canon Medical Systems Co. Ltd.

Author information

Authors and Affiliations

  1. Diagnostic Radiology, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima, 734-8551, Japan

    Yuko Nakamura, Keigo Narita, Toru Higaki, Motonori Akagi, Yukiko Honda & Kazuo Awai

Authors
  1. Yuko Nakamura
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  2. Keigo Narita
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  3. Toru Higaki
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  4. Motonori Akagi
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  5. Yukiko Honda
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  6. Kazuo Awai
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Corresponding author

Correspondence to Yuko Nakamura.

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Guarantor

The scientific guarantor of this publication is Dr. Kazuo Awai.

Conflict of interest

The authors of this manuscript declare relationships with the following companies: Canon Medical Systems Co. Ltd for Kazuo Awai.

The other authors declare that they have no conflict of interest.

Statistics and biometry

No complex statistical methods were necessary for this paper.

Informed consent

Our institutional review board approved our study in which we applied 70% of the standard radiation dose plus deep learning reconstruction (DLR) to obtain hepatic dynamic CT scans as an observational study. Hybrid iterative reconstruction (hybrid-IR) and model-based IR (MBIR) images were reconstructed from the existing raw data. Thus, prior informed patient consent was waived.

Ethical approval

Institutional Review Board approval was obtained.

Methodology

• retrospective

• diagnostic study

• performed at one institution

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Nakamura, Y., Narita, K., Higaki, T. et al. Diagnostic value of deep learning reconstruction for radiation dose reduction at abdominal ultra-high-resolution CT. Eur Radiol 31, 4700–4709 (2021). https://doi.org/10.1007/s00330-020-07566-2

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  • DOI: https://doi.org/10.1007/s00330-020-07566-2

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