Adaptive and Iterative Learning with Multi-perspective Regularizations for Metal Artifact Reduction.

Zhang, J; Mao, H; Chang, D; Yu, H; Wu, W; Shen, D

Adaptive and Iterative Learning with Multi-perspective Regularizations for Metal Artifact Reduction.

Zhang, J

Mao, H Chang, D Yu, H Wu, W Shen, D

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Publisher:: Institute of Electrical and Electronics Engineers (IEEE)
Publication Type:: Journal Article
Citation:: IEEE Trans Med Imaging, 2024, PP, (99), pp. 1-1
Issue Date:: 2024-04-30

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Field	Value	Language
dc.contributor.author	Zhang, J https://orcid.org/0000-0001-5048-5606
dc.contributor.author	Mao, H
dc.contributor.author	Chang, D
dc.contributor.author	Yu, H
dc.contributor.author	Wu, W
dc.contributor.author	Shen, D
dc.date.accessioned	2024-08-21T05:45:17Z
dc.date.available	2024-08-21T05:45:17Z
dc.date.issued	2024-04-30
dc.identifier.citation	IEEE Trans Med Imaging, 2024, PP, (99), pp. 1-1
dc.identifier.issn	0278-0062
dc.identifier.issn	1558-254X
dc.identifier.uri	http://hdl.handle.net/10453/180512
dc.description.abstract	Metal artifact reduction (MAR) is important for clinical diagnosis with CT images. The existing state-of-the-art deep learning methods usually suppress metal artifacts in sinogram or image domains or both. However, their performance is limited by the inherent characteristics of the two domains, i.e., the errors introduced by local manipulations in the sinogram domain would propagate throughout the whole image during backprojection and lead to serious secondary artifacts, while it is difficult to distinguish artifacts from actual image features in the image domain. To alleviate these limitations, this study analyzes the desirable properties of wavelet transform in-depth and proposes to perform MAR in the wavelet domain. First, wavelet transform yields components that possess spatial correspondence with the image, thereby preventing the spread of local errors to avoid secondary artifacts. Second, using wavelet transform could facilitate identification of artifacts from image since metal artifacts are mainly high-frequency signals. Taking these advantages of the wavelet transform, this paper decomposes an image into multiple wavelet components and introduces multi-perspective regularizations into the proposed MAR model. To improve the transparency and validity of the model, all the modules in the proposed MAR model are designed to reflect their mathematical meanings. In addition, an adaptive wavelet module is also utilized to enhance the flexibility of the model. To optimize the model, an iterative algorithm is developed. The evaluation on both synthetic and real clinical datasets consistently confirms the superior performance of the proposed method over the competing methods.
dc.format	Print-Electronic
dc.language	eng
dc.publisher	Institute of Electrical and Electronics Engineers (IEEE)
dc.relation.ispartof	IEEE Trans Med Imaging
dc.relation.isbasedon	10.1109/TMI.2024.3395348
dc.rights	info:eu-repo/semantics/restrictedAccess
dc.subject	08 Information and Computing Sciences, 09 Engineering
dc.subject.classification	Nuclear Medicine & Medical Imaging
dc.subject.classification	40 Engineering
dc.subject.classification	46 Information and computing sciences
dc.title	Adaptive and Iterative Learning with Multi-perspective Regularizations for Metal Artifact Reduction.
dc.type	Journal Article
utslib.citation.volume	PP
utslib.location.activity	United States
utslib.for	08 Information and Computing Sciences
utslib.for	09 Engineering
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology/A/DRsch The Data Science Institute
utslib.copyright.status	recently_added	*
dc.date.updated	2024-08-21T05:45:14Z
pubs.issue	99
pubs.publication-status	Published online
pubs.volume	PP
utslib.citation.issue	99

Abstract:

Metal artifact reduction (MAR) is important for clinical diagnosis with CT images. The existing state-of-the-art deep learning methods usually suppress metal artifacts in sinogram or image domains or both. However, their performance is limited by the inherent characteristics of the two domains, i.e., the errors introduced by local manipulations in the sinogram domain would propagate throughout the whole image during backprojection and lead to serious secondary artifacts, while it is difficult to distinguish artifacts from actual image features in the image domain. To alleviate these limitations, this study analyzes the desirable properties of wavelet transform in-depth and proposes to perform MAR in the wavelet domain. First, wavelet transform yields components that possess spatial correspondence with the image, thereby preventing the spread of local errors to avoid secondary artifacts. Second, using wavelet transform could facilitate identification of artifacts from image since metal artifacts are mainly high-frequency signals. Taking these advantages of the wavelet transform, this paper decomposes an image into multiple wavelet components and introduces multi-perspective regularizations into the proposed MAR model. To improve the transparency and validity of the model, all the modules in the proposed MAR model are designed to reflect their mathematical meanings. In addition, an adaptive wavelet module is also utilized to enhance the flexibility of the model. To optimize the model, an iterative algorithm is developed. The evaluation on both synthetic and real clinical datasets consistently confirms the superior performance of the proposed method over the competing methods.

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http://hdl.handle.net/10453/180512