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

  • Imaging Informatics and Artificial Intelligence
  • Published:
European Radiology Aims and scope Submit manuscript

A Correction to this article was published on 27 May 2019

This article has been updated

Abstract

Objectives

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).

Methods

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.

Results

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.

Conclusions

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.

Abbreviations

AiCE:

Advanced Intelligent Clear-IQ Engine

AIDR3D:

Adaptive iterative dose reduction 3-dimensional

CNR:

Contrast-to-noise ratio

CTDIvol :

CT dose index

DCNN:

Deep convolutional neural networks

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

HU:

Hounsfield units

Hybrid-IR:

Hybrid iterative reconstruction

MBIR:

Model-based iterative reconstruction

PVP:

Portal venous phase

ROI:

Region of interest

SD:

Standard deviation

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, Japan

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

  2. Canon Medical Research USA, Inc., Vernon Hills, IL, USA

    Jian Zhou & Zhou Yu

  3. Canon Medical Systems Co. Ltd., Otawara, Japan

    Naruomi Akino

Authors
  1. Motonori Akagi
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  2. Yuko Nakamura
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  4. Keigo Narita
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  5. Yukiko Honda
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  6. Jian Zhou
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  7. Zhou Yu
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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 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.

Methodology

• 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). https://doi.org/10.1007/s00330-019-06170-3

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