Deep Image Prior Acceleration Method for Target Offset in Low-dose CT Images Denoising

ZENG Li; XIONG Xilin; CHEN Wei

doi:10.11999/JEIT220551

Volume 45 Issue 6

Jun. 2023

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Article Navigation > Journal of Electronics & Information Technology > 2023 > 45(6): 2188-2196

ZENG Li, XIONG Xilin, CHEN Wei. Deep Image Prior Acceleration Method for Target Offset in Low-dose CT Images Denoising[J]. Journal of Electronics & Information Technology, 2023, 45(6): 2188-2196. doi: 10.11999/JEIT220551

Citation:

ZENG Li, XIONG Xilin, CHEN Wei. Deep Image Prior Acceleration Method for Target Offset in Low-dose CT Images Denoising[J]. Journal of Electronics & Information Technology, 2023, 45(6): 2188-2196. doi: 10.11999/JEIT220551

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Deep Image Prior Acceleration Method for Target Offset in Low-dose CT Images Denoising

doi: 10.11999/JEIT220551

ZENG Li^{1, 2
,
,},
XIONG Xilin^{1, 2},
CHEN Wei³

1.
College of Mathematics and Statistics, Chongqing University, Chongqing 401331, China
2.
Engineering Research Center of Industrial Computed Tomography Nondestructive Testing of the Education Ministry of China, Chongqing University, Chongqing 400044, China
3.
Department of Radiology, Southwest Hospital, Army Medical University, Chongqing 400038, China

Funds: The National Natural Science Foundation of China (61771003), The Graduate Scientific Research and Innovation Foundation of Chongqing (CYS19026)

Received Date: 2022-05-05
Rev Recd Date: 2022-10-14

Available Online: 2022-10-21

Publish Date: 2023-06-10

Abstract

Abstract

Low Dose CT (LDCT) images can significantly reduce the X-ray radiation dose, but there is a lot of noise that affects doctors' diagnosis. Deep Image Prior (DIP) is an unsupervised deep learning algorithm that uses random tensor as the input of neural network and iterates with a single LDCT image as the target. However, DIP needs thousands of iterations to get the best denoised results, resulting in the slow running speed of this method. Therefore, a DIP acceleration method for target offset in low-dose CT images is proposed, which aims to improve the running speed while maintaining the quality of denoised image. According to the similarity of LDCT slice images of an organ (such as lungs), the algorithm associates independent networks whose target images are different slices by inheriting parameters, updates the network parameters corresponding to the current slice based on the network parameters corresponding to the previous slice, and takes the network parameters corresponding to the current slice as the basis of next network corresponding to next slice to update parameters; Since the input of DIP network is a fixed random tensor, which is different from the target image greatly, this paper uses the LDCT image preprocessed by the traditional models as the network input to improve further the network iteration speed. Experiments show that the proposed acceleration algorithm can improve the iteration speed by 10.45% compared with the original DIP network without traditional model preprocessing. When LDCT preprocessed by Relative Total Variation (RTV) model is used as the network input, the image peak signal-to-noise ratio can not only reach 29.13, but also the total iterative speed can be increased by 94.31%. Therefore, this algorithm can greatly improve the running speed while maintaining the denoised quality of DIP, especially when the CT image preprocessed by RTV model is used as the network input, the effect of improving the running speed is more obvious.
- Image denoising,
- Low Dose CT (LDCT),
- Deep learning,
- Deep Image Prior (DIP),
- Acceleration method

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References(16)

References

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