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Deep learning reconstruction improves image quality of abdominal ultra-high-resolution CT

A Correction to this article was published on 27 May 2019

This article has been updated



Deep learning reconstruction (DLR) is a new reconstruction method; it introduces deep convolutional neural networks into the reconstruction flow. This study was conducted in order to examine the clinical applicability of abdominal ultra-high-resolution CT (U-HRCT) exams reconstructed with a new DLR in comparison to hybrid and model-based iterative reconstruction (hybrid-IR, MBIR).


Our retrospective study included 46 patients seen between December 2017 and April 2018. A radiologist recorded the standard deviation of attenuation in the paraspinal muscle as the image noise and calculated the contrast-to-noise ratio (CNR) for the aorta, portal vein, and liver. The overall image quality was assessed by two other radiologists and graded on a 5-point confidence scale ranging from 1 (unacceptable) to 5 (excellent). The difference between CT images subjected to hybrid-IR, MBIR, and DLR was compared.


The image noise was significantly lower and the CNR was significantly higher on DLR than hybrid-IR and MBIR images (p < 0.01). DLR images received the highest and MBIR images the lowest scores for overall image quality.


DLR improved the quality of abdominal U-HRCT images.

Key Points

The potential degradation due to increased noise may prevent implementation of ultra-high-resolution CT in the abdomen.

Image noise and overall image quality for hepatic ultra-high-resolution CT images improved with deep learning reconstruction as compared to hybrid- and model-based iterative reconstruction.

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Change history

  • 27 May 2019

    The original version of this article, published on 11 April 2019, unfortunately, contained a mistake. The following correction has therefore been made in the original: The image in Fig. 3c was wrong. The corrected figure is given below. The original article has been corrected.



Advanced Intelligent Clear-IQ Engine


Adaptive iterative dose reduction 3-dimensional


Contrast-to-noise ratio

CTDIvol :

CT dose index


Deep convolutional neural networks


Digital Imaging and Communications in Medicine


Dose-length product


Deep learning reconstruction


Equilibrium phase


Forward-projected model-based iterative reconstruction solution


Hepatic arterial phase


Hounsfield units


Hybrid iterative reconstruction


Model-based iterative reconstruction


Portal venous phase


Region of interest


Standard deviation


Size-specific dose estimate


Ultra-high-resolution computed tomography


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

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Corresponding author

Correspondence to Yuko Nakamura.

Ethics declarations


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 and Naruomi Akino and Canon Medical Research USA for Jian Zhou and Zhou Yu. Naruomi Akino, Jian Zhou, and Zhou Yu contributed to this study for manuscript editing regarding the description of deep learning reconstruction (DLR) algorithm. The authors who are not employees of Canon Medical Systems had control of inclusion of any data and information that might present a conflict of interest for those authors who are employees of Canon Medical Systems. 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

Written informed consent was not required for this study because this study used existing CT images including raw data.

Ethical approval

Institutional Review Board approval was obtained.


• retrospective

• diagnostic study

• performed at one institution

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The original version of this article was revised: The image in Figure 3c was wrong.

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Akagi, M., Nakamura, Y., Higaki, T. et al. Deep learning reconstruction improves image quality of abdominal ultra-high-resolution CT. Eur Radiol 29, 6163–6171 (2019).

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  • Liver
  • Neural networks (computer)
  • X-ray computed tomography
  • Machine learning
  • Artificial intelligence