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Low-Dose CT Denoising Algorithm Based on Image Cartoon Texture Decomposition.

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Title: Low-Dose CT Denoising Algorithm Based on Image Cartoon Texture Decomposition.
Authors: Chen, Hao1,2 (AUTHOR), Liu, Yi1,2 (AUTHOR), Zhang, Pengcheng1,2 (AUTHOR), Kang, Jiaqi1,2 (AUTHOR), Li, Zhiyuan1,2 (AUTHOR), Cheng, Weiting1,2 (AUTHOR), Gui, Zhiguo1,2 (AUTHOR) gzgtg@163.com
Source: Circuits, Systems & Signal Processing. May2024, Vol. 43 Issue 5, p3073-3101. 29p.
Subjects: Image denoising, Texture analysis (Image processing), Convolutional neural networks, Quantum noise, Computed tomography, Deep learning, Algorithms
Abstract: Low-dose computed tomography (LDCT) technology has attracted more and more attention in the field of medical imaging because of the reduction of radiation damage to the human body. However, the large amount of quantum noise contained in LDCT images can affect physicians' judgment. To solve the problem of large amounts of quantum noise and artifacts in LDCT images, a convolutional neural network denoising model based on cartoon texture decomposition of images (CATCNN) is developed in this study based on deep learning. The model first uses a U-Net-based image decomposition sub-network to decompose LDCT images into cartoon images and texture images. The texture images are then denoised using a texture denoising sub-network based on edge protection and Efficient Channel Attention, finally, cartoon images are summed with the denoised texture images to obtain images with improved quality. Our experimental results demonstrate that the proposed model outperforms existing technologies, achieving a peak signal-to-noise ratio value of 33.4666 dB and a structural similarity value of 0.9193. The visual and quantitative evaluation results suggest that the CATCNN model effectively improves the quality of LDCT images. [ABSTRACT FROM AUTHOR]
Copyright of Circuits, Systems & Signal Processing is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Low-Dose CT Denoising Algorithm Based on Image Cartoon Texture Decomposition.
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  Data: <searchLink fieldCode="JN" term="%22Circuits%2C+Systems+%26+Signal+Processing%22">Circuits, Systems & Signal Processing</searchLink>. May2024, Vol. 43 Issue 5, p3073-3101. 29p.
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  Data: <searchLink fieldCode="DE" term="%22Image+denoising%22">Image denoising</searchLink><br /><searchLink fieldCode="DE" term="%22Texture+analysis+%28Image+processing%29%22">Texture analysis (Image processing)</searchLink><br /><searchLink fieldCode="DE" term="%22Convolutional+neural+networks%22">Convolutional neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Quantum+noise%22">Quantum noise</searchLink><br /><searchLink fieldCode="DE" term="%22Computed+tomography%22">Computed tomography</searchLink><br /><searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink><br /><searchLink fieldCode="DE" term="%22Algorithms%22">Algorithms</searchLink>
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  Data: Low-dose computed tomography (LDCT) technology has attracted more and more attention in the field of medical imaging because of the reduction of radiation damage to the human body. However, the large amount of quantum noise contained in LDCT images can affect physicians' judgment. To solve the problem of large amounts of quantum noise and artifacts in LDCT images, a convolutional neural network denoising model based on cartoon texture decomposition of images (CATCNN) is developed in this study based on deep learning. The model first uses a U-Net-based image decomposition sub-network to decompose LDCT images into cartoon images and texture images. The texture images are then denoised using a texture denoising sub-network based on edge protection and Efficient Channel Attention, finally, cartoon images are summed with the denoised texture images to obtain images with improved quality. Our experimental results demonstrate that the proposed model outperforms existing technologies, achieving a peak signal-to-noise ratio value of 33.4666 dB and a structural similarity value of 0.9193. The visual and quantitative evaluation results suggest that the CATCNN model effectively improves the quality of LDCT images. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Circuits, Systems & Signal Processing is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1007/s00034-023-02594-x
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        Text: English
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      – SubjectFull: Convolutional neural networks
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      – SubjectFull: Quantum noise
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      – SubjectFull: Computed tomography
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      – SubjectFull: Deep learning
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      – SubjectFull: Algorithms
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      – TitleFull: Low-Dose CT Denoising Algorithm Based on Image Cartoon Texture Decomposition.
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            NameFull: Chen, Hao
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            – D: 01
              M: 05
              Text: May2024
              Type: published
              Y: 2024
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