Sparse CT Reconstruction Based on Adversarial Residual Dense Deep Neural Network

DU Congcong; QIAO Zhiwei

doi:10.15953/j.ctta.2021.032

Volume 31 Issue 2

Mar. 2022

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CT Theory and Applications > 2022 > 31(2): 163-172. > DOI: 10.15953/j.ctta.2021.032

DU C C, QIAO Z W. Sparse CT reconstruction based on adversarial residual dense deep neural network[J]. CT Theory and Applications, 2022, 31(2): 163-172. DOI: 10.15953/j.ctta.2021.032. (in Chinese).

Citation:

DU C C, QIAO Z W. Sparse CT reconstruction based on adversarial residual dense deep neural network[J]. CT Theory and Applications, 2022, 31(2): 163-172. DOI: 10.15953/j.ctta.2021.032. (in Chinese).

Citation:

DU C C, QIAO Z W. Sparse CT reconstruction based on adversarial residual dense deep neural network[J]. CT Theory and Applications, 2022, 31(2): 163-172. DOI: 10.15953/j.ctta.2021.032. (in Chinese).

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Sparse CT Reconstruction Based on Adversarial Residual Dense Deep Neural Network

DU Congcong,
QIAO Zhiwei^,

School of Computer and Information Technology, Shanxi University, Taiyuan 030006, China

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Received Date: November 07, 2021
Accepted Date: November 17, 2021
Available Online: November 24, 2021
Published Date: March 31, 2022

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Abstract

Abstract

To solve the problem of severe streak artifacts in sparse-view computed tomography (CT) reconstruction, in this paper we propose a method which is based on the adversarial residual dense deep neural network to acquire high-quality sparse-view CT reconstruction. The UNet that combines residual connectioin, dense connection, adversarial mechanism and attention mechanism is designed, which is trained through large-scale training data composed of streak artifact images and high-quality images to suppress streak artifacts. First, the filtered back projection (FBP) algorithm is used to reconstruct CT images with streak artifacts from sparse projections, then these images are inputed into the deep network, which can suppress streak artifacts to output high-quality images. The experimental results show that, compared with the existing deep learning algorithms, the image reconstructed by the proposed new network possesses higher accuracy and can suppress streak artifacts better.
- sparse reconstruction,
- CT,
- UNet,
- adversarial mechanism,
- attention mechanism

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Sparse CT Reconstruction Based on Adversarial Residual Dense Deep Neural Network

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