Fast EPRI Imaging Based on 3MNet Denoising Network

QIAN Peizhang; QIAO Zhiwei; DU Congcong

doi:10.15953/j.ctta.2022.065

Volume 32 Issue 1

Jan. 2023

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CT Theory and Applications > 2023 > 32(1): 55-66. > DOI: 10.15953/j.ctta.2022.065

QIAN P Z, QIAO Z W, DU C C. Fast EPRI Imaging Based on 3MNet Denoising Network[J]. CT Theory and Applications, 2023, 32(1): 55-66. DOI: 10.15953/j.ctta.2022.065. (in Chinese).

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QIAN P Z, QIAO Z W, DU C C. Fast EPRI Imaging Based on 3MNet Denoising Network[J]. CT Theory and Applications, 2023, 32(1): 55-66. DOI: 10.15953/j.ctta.2022.065. (in Chinese).

Citation:

QIAN P Z, QIAO Z W, DU C C. Fast EPRI Imaging Based on 3MNet Denoising Network[J]. CT Theory and Applications, 2023, 32(1): 55-66. DOI: 10.15953/j.ctta.2022.065. (in Chinese).

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Fast EPRI Imaging Based on 3MNet Denoising Network

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

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Received Date: April 14, 2022
Accepted Date: May 08, 2022
Available Online: May 15, 2022
Published Date: January 30, 2023

Graphical Abstract

Abstract

Abstract

Electron paramagnetic resonance imaging (EPRI) is an advanced technique for oxygen imaging. The current bottleneck in EPRI is the slow scanning speed, due to the fact that the projection signal at each angle needs to be repeated thousands of times to suppress random noise. One way to achieve fast scanning is to reduce the number of repeated projection signal acquisitions; however, this, in turn, reduces the signal-to-noise ratio of the projection signal and results in a noisy reconstructed image. To effectively suppress the noise in the reconstructed image, this study proposes a multi-channel, multi-scale, multi-concatenation, and convolutional network (3MNet) based image denoising network to achieve high accuracy and fast EPRI imaging. The proposed network consists of three sub-networks. The first sub-network is an attention-based convolutional network, whose output feature images are stitched with the input images to form the input of the back-end network. The second sub-network is a three-channel convolutional network. Finally, the third sub-network is a multi-scale convolutional network. The experimental results demonstrate that the proposed 3MNet network can achieve high accuracy in denoising EPRI images and fast imaging.
- deep learning,
- convolutional neural network,
- EPRI,
- rapid imaging

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References (33)

References

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