Texture‐aware dual domain mapping model for low‐dose CT reconstruction
Background Remarkable progress has been made for low‐dose computed tomography (CT) reconstruction tasks by applying deep learning techniques. However, establishing an intrinsic link between deep learning techniques and CT texture preservation is still one of the significant challenges for researcher...
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| Veröffentlicht in: | Medical physics (Lancaster) Jg. 49; H. 6; S. 3860 - 3873 |
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01.06.2022
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| ISSN: | 0094-2405, 2473-4209, 2473-4209 |
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| Abstract | Background
Remarkable progress has been made for low‐dose computed tomography (CT) reconstruction tasks by applying deep learning techniques. However, establishing an intrinsic link between deep learning techniques and CT texture preservation is still one of the significant challenges for researchers to further improve the effect of low‐dose CT (LDCT) reconstruction.
Purpose
Most of the existing deep learning‐based LDCT reconstruction methods are derived from popular frameworks, and most models focus on the image domain. Even few existing methods start with dual domains (sinogram and image) by considering the processing of the data itself, the final performances are limited due to the lack of perception of textures. With this in mind, we propose a method for texture perception on dual domains, so that the reconstruction process can be uniformly driven by visual effects.
Methods
The proposed method involves the processing of two domains: the sinogram domain and the image domain. For the sinogram domain, we have designed a novel dilated residual network (S‐DRN) which aims to increase the receptive field to obtain multiscale information. For the image domain, we propose a self‐attention (SA) residual encoder & decoder network (SRED‐Net) as the denoising network for obtaining much acceptable edges and textures. In addition, the composite loss function composed of the feature loss constructed by the proposed boundary and texture feature‐aware network (BTFAN) and the mean square error (MSE) can obtain a higher image quality while retaining more details and fewer artifacts, thereby obtaining better visual image quality.
Results
The proposed method was validated using both the American association of physicists in medicine (AAPM)‐Mayo clinic LDCT data sets and a real clinic data. Experimental results demonstrated that the new method has achieved the state‐of‐the‐art performance on objective indicators and visual metrics in terms of denoising and texture restoration.
Conclusions
Compared with single‐domain or existing dual‐domain processing strategies, the proposed texture‐aware dual domain mapping network (TADDM‐Net) can much better improve the visual effect of reconstructed CT images. Meantime, we also provide much intuitive evidence in terms of model interpretability. |
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| AbstractList | Remarkable progress has been made for low-dose computed tomography (CT) reconstruction tasks by applying deep learning techniques. However, establishing an intrinsic link between deep learning techniques and CT texture preservation is still one of the significant challenges for researchers to further improve the effect of low-dose CT (LDCT) reconstruction.
Most of the existing deep learning-based LDCT reconstruction methods are derived from popular frameworks, and most models focus on the image domain. Even few existing methods start with dual domains (sinogram and image) by considering the processing of the data itself, the final performances are limited due to the lack of perception of textures. With this in mind, we propose a method for texture perception on dual domains, so that the reconstruction process can be uniformly driven by visual effects.
The proposed method involves the processing of two domains: the sinogram domain and the image domain. For the sinogram domain, we have designed a novel dilated residual network (S-DRN) which aims to increase the receptive field to obtain multiscale information. For the image domain, we propose a self-attention (SA) residual encoder & decoder network (SRED-Net) as the denoising network for obtaining much acceptable edges and textures. In addition, the composite loss function composed of the feature loss constructed by the proposed boundary and texture feature-aware network (BTFAN) and the mean square error (MSE) can obtain a higher image quality while retaining more details and fewer artifacts, thereby obtaining better visual image quality.
The proposed method was validated using both the American association of physicists in medicine (AAPM)-Mayo clinic LDCT data sets and a real clinic data. Experimental results demonstrated that the new method has achieved the state-of-the-art performance on objective indicators and visual metrics in terms of denoising and texture restoration.
Compared with single-domain or existing dual-domain processing strategies, the proposed texture-aware dual domain mapping network (TADDM-Net) can much better improve the visual effect of reconstructed CT images. Meantime, we also provide much intuitive evidence in terms of model interpretability. Remarkable progress has been made for low-dose computed tomography (CT) reconstruction tasks by applying deep learning techniques. However, establishing an intrinsic link between deep learning techniques and CT texture preservation is still one of the significant challenges for researchers to further improve the effect of low-dose CT (LDCT) reconstruction.BACKGROUNDRemarkable progress has been made for low-dose computed tomography (CT) reconstruction tasks by applying deep learning techniques. However, establishing an intrinsic link between deep learning techniques and CT texture preservation is still one of the significant challenges for researchers to further improve the effect of low-dose CT (LDCT) reconstruction.Most of the existing deep learning-based LDCT reconstruction methods are derived from popular frameworks, and most models focus on the image domain. Even few existing methods start with dual domains (sinogram and image) by considering the processing of the data itself, the final performances are limited due to the lack of perception of textures. With this in mind, we propose a method for texture perception on dual domains, so that the reconstruction process can be uniformly driven by visual effects.PURPOSEMost of the existing deep learning-based LDCT reconstruction methods are derived from popular frameworks, and most models focus on the image domain. Even few existing methods start with dual domains (sinogram and image) by considering the processing of the data itself, the final performances are limited due to the lack of perception of textures. With this in mind, we propose a method for texture perception on dual domains, so that the reconstruction process can be uniformly driven by visual effects.The proposed method involves the processing of two domains: the sinogram domain and the image domain. For the sinogram domain, we have designed a novel dilated residual network (S-DRN) which aims to increase the receptive field to obtain multiscale information. For the image domain, we propose a self-attention (SA) residual encoder & decoder network (SRED-Net) as the denoising network for obtaining much acceptable edges and textures. In addition, the composite loss function composed of the feature loss constructed by the proposed boundary and texture feature-aware network (BTFAN) and the mean square error (MSE) can obtain a higher image quality while retaining more details and fewer artifacts, thereby obtaining better visual image quality.METHODSThe proposed method involves the processing of two domains: the sinogram domain and the image domain. For the sinogram domain, we have designed a novel dilated residual network (S-DRN) which aims to increase the receptive field to obtain multiscale information. For the image domain, we propose a self-attention (SA) residual encoder & decoder network (SRED-Net) as the denoising network for obtaining much acceptable edges and textures. In addition, the composite loss function composed of the feature loss constructed by the proposed boundary and texture feature-aware network (BTFAN) and the mean square error (MSE) can obtain a higher image quality while retaining more details and fewer artifacts, thereby obtaining better visual image quality.The proposed method was validated using both the American association of physicists in medicine (AAPM)-Mayo clinic LDCT data sets and a real clinic data. Experimental results demonstrated that the new method has achieved the state-of-the-art performance on objective indicators and visual metrics in terms of denoising and texture restoration.RESULTSThe proposed method was validated using both the American association of physicists in medicine (AAPM)-Mayo clinic LDCT data sets and a real clinic data. Experimental results demonstrated that the new method has achieved the state-of-the-art performance on objective indicators and visual metrics in terms of denoising and texture restoration.Compared with single-domain or existing dual-domain processing strategies, the proposed texture-aware dual domain mapping network (TADDM-Net) can much better improve the visual effect of reconstructed CT images. Meantime, we also provide much intuitive evidence in terms of model interpretability.CONCLUSIONSCompared with single-domain or existing dual-domain processing strategies, the proposed texture-aware dual domain mapping network (TADDM-Net) can much better improve the visual effect of reconstructed CT images. Meantime, we also provide much intuitive evidence in terms of model interpretability. Background Remarkable progress has been made for low‐dose computed tomography (CT) reconstruction tasks by applying deep learning techniques. However, establishing an intrinsic link between deep learning techniques and CT texture preservation is still one of the significant challenges for researchers to further improve the effect of low‐dose CT (LDCT) reconstruction. Purpose Most of the existing deep learning‐based LDCT reconstruction methods are derived from popular frameworks, and most models focus on the image domain. Even few existing methods start with dual domains (sinogram and image) by considering the processing of the data itself, the final performances are limited due to the lack of perception of textures. With this in mind, we propose a method for texture perception on dual domains, so that the reconstruction process can be uniformly driven by visual effects. Methods The proposed method involves the processing of two domains: the sinogram domain and the image domain. For the sinogram domain, we have designed a novel dilated residual network (S‐DRN) which aims to increase the receptive field to obtain multiscale information. For the image domain, we propose a self‐attention (SA) residual encoder & decoder network (SRED‐Net) as the denoising network for obtaining much acceptable edges and textures. In addition, the composite loss function composed of the feature loss constructed by the proposed boundary and texture feature‐aware network (BTFAN) and the mean square error (MSE) can obtain a higher image quality while retaining more details and fewer artifacts, thereby obtaining better visual image quality. Results The proposed method was validated using both the American association of physicists in medicine (AAPM)‐Mayo clinic LDCT data sets and a real clinic data. Experimental results demonstrated that the new method has achieved the state‐of‐the‐art performance on objective indicators and visual metrics in terms of denoising and texture restoration. Conclusions Compared with single‐domain or existing dual‐domain processing strategies, the proposed texture‐aware dual domain mapping network (TADDM‐Net) can much better improve the visual effect of reconstructed CT images. Meantime, we also provide much intuitive evidence in terms of model interpretability. |
| Author | Guo, Lei Liu, Wanquan Wang, Huafeng Ma, Jianhua Zhao, Xuemei Li, Lihong C. |
| Author_xml | – sequence: 1 givenname: Huafeng surname: Wang fullname: Wang, Huafeng organization: North China University of Technology – sequence: 2 givenname: Xuemei surname: Zhao fullname: Zhao, Xuemei organization: North China University of Technology – sequence: 3 givenname: Wanquan surname: Liu fullname: Liu, Wanquan email: liuwq63@mail.sysu.edu.cn organization: Sun Yat‐sen University – sequence: 4 givenname: Lihong C. surname: Li fullname: Li, Lihong C. organization: Department of Engineering and Environmental Science, City University of New York at College of Staten Island – sequence: 5 givenname: Jianhua surname: Ma fullname: Ma, Jianhua organization: Department of Biomedical Engineering, Southern Medical University – sequence: 6 givenname: Lei surname: Guo fullname: Guo, Lei organization: Beihang University |
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Remarkable progress has been made for low‐dose computed tomography (CT) reconstruction tasks by applying deep learning techniques. However,... Remarkable progress has been made for low-dose computed tomography (CT) reconstruction tasks by applying deep learning techniques. However, establishing an... |
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| StartPage | 3860 |
| SubjectTerms | CT reconstruction denoising dual domain texture preservation |
| Title | Texture‐aware dual domain mapping model for low‐dose CT reconstruction |
| URI | https://onlinelibrary.wiley.com/doi/abs/10.1002%2Fmp.15607 https://www.ncbi.nlm.nih.gov/pubmed/35297051 https://www.proquest.com/docview/2640321715 |
| Volume | 49 |
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