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01-11-2019 | Computed Tomography | Imaging Informatics and Artificial Intelligence

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

Authors: Motonori Akagi, Yuko Nakamura, Toru Higaki, Keigo Narita, Yukiko Honda, Jian Zhou, Zhou Yu, Naruomi Akino, Kazuo Awai

Published in: European Radiology | Issue 11/2019

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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Title: Deep learning reconstruction improves image quality of abdominal ultra-high-resolution CT
Authors: Motonori Akagi
Yuko Nakamura
Toru Higaki
Keigo Narita
Yukiko Honda
Jian Zhou
Zhou Yu
Naruomi Akino
Kazuo Awai
Publication date: 01-11-2019
Publisher: Springer Berlin Heidelberg
Keywords: Computed Tomography
Computed Tomography
Published in: European Radiology / Issue 11/2019
Print ISSN: 0938-7994
Electronic ISSN: 1432-1084
DOI: https://doi.org/10.1007/s00330-019-06170-3

Keynote webinar | Spotlight on sleep in brain health

Springer Medicine

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

Abstract

Objectives

Methods

Results

Conclusions

Key Points

Keynote webinar | Spotlight on sleep in brain health

Springer Medicine

Abstract

Objectives

Methods

Results

Conclusions

Key Points

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