Deep Encoder-Decoder Adversarial Reconstruction (DEAR) Network for 3D CT from Few-View Data

X-ray computed tomography (CT) is widely used in clinical practice. The involved ionizing X-ray radiation, however, could increase cancer risk. Hence, the reduction of the radiation dose has been an important topic in recent years. Few-view CT image reconstruction is one of the main ways to minimize...

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Bibliographic Details
Published in:Bioengineering (Basel) Vol. 6; no. 4; p. 111
Main Authors: Xie, Huidong, Shan, Hongming, Wang, Ge
Format: Journal Article
Language:English
Published: Switzerland MDPI AG 09.12.2019
MDPI
Subjects:
Algorithms
Big Data
Bioengineering
Coders
Computed tomography
Datasets
Deep learning
Design
Encoders-Decoders
Image processing
Image quality
Image reconstruction
Information processing
Ionizing radiation
Lung cancer
Medical imaging
Medical screening
Methods
Radiation
Radiation dosage
Tomography
X-rays
few-view CT
deep learning
generative adversarial network (GAN)
machine learning
deep encoder-decoder adversarial network (DEAR)
sparse-view CT
ISSN:2306-5354, 2306-5354
Online Access:Get full text
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Summary:X-ray computed tomography (CT) is widely used in clinical practice. The involved ionizing X-ray radiation, however, could increase cancer risk. Hence, the reduction of the radiation dose has been an important topic in recent years. Few-view CT image reconstruction is one of the main ways to minimize radiation dose and potentially allow a stationary CT architecture. In this paper, we propose a deep encoder-decoder adversarial reconstruction (DEAR) network for 3D CT image reconstruction from few-view data. Since the artifacts caused by few-view reconstruction appear in 3D instead of 2D geometry, a 3D deep network has a great potential for improving the image quality in a data driven fashion. More specifically, our proposed DEAR-3D network aims at reconstructing 3D volume directly from clinical 3D spiral cone-beam image data. DEAR is validated on a publicly available abdominal CT dataset prepared and authorized by Mayo Clinic. Compared with other 2D deep learning methods, the proposed DEAR-3D network can utilize 3D information to produce promising reconstruction results.
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ISSN:2306-5354
2306-5354
DOI:10.3390/bioengineering6040111