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

  • Motonori Akagi
  • Yuko NakamuraEmail author
  • Toru Higaki
  • Keigo Narita
  • Yukiko Honda
  • Jian Zhou
  • Zhou Yu
  • Naruomi Akino
  • Kazuo Awai
Imaging Informatics and Artificial Intelligence
  • 457 Downloads

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.

Keywords

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

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

Notes

Funding

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

Compliance with ethical standards

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

Supplementary material

330_2019_6170_MOESM1_ESM.doc (58 kb)
ESM 1 (DOC 57 kb)

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Copyright information

© European Society of Radiology 2019
corrected publication 2019

Authors and Affiliations

  1. 1.Diagnostic RadiologyHiroshima UniversityHiroshimaJapan
  2. 2.Canon Medical Research USA, Inc.Vernon HillsUSA
  3. 3.Canon Medical Systems Co. Ltd.OtawaraJapan

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