Academic Journal
Time-frequency domain prior constrained deep unfolding network for low-dose CT reconstruction.
| Τίτλος: | Time-frequency domain prior constrained deep unfolding network for low-dose CT reconstruction. |
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| Συγγραφείς: | Zhang, Xiong, Zhang, Xinbo, Li, Xinzhong, Ali, Zulfiqur, Wang, Yue, Shangguan, Hong, Cui, Xueying |
| Πηγή: | Journal of X-Ray Science & Technology; Sep2025, Vol. 33 Issue 5, p819-830, 12p |
| Θεματικοί όροι: | Deep learning, Computed tomography, Iterative methods (Mathematics), Image processing, Convolutional neural networks, Image enhancement (Imaging systems) |
| Περίληψη: | Background: Low-dose computed tomography (LDCT) effectively reduces the risk of malignant disease; however, reducing the radiation dose introduces additional noise and stripe artifacts in the CT imaging process. While Convolutional Neural Networks (CNN) have demonstrated performance advantages in LDCT imaging tasks, their end-to-end network architecture limits adaptability to CT reconstruction tasks, leaving room for further performance improvement. Objective: To propose a low-dose CT reconstruction network based on the iterative algorithms, incorporating an interpretable network architecture to achieve superior reconstruction performance. Methods: To better adapt to CT reconstruction tasks, we proposed an interpretable deep unfolding network leveraging time-frequency and image domain priors to fully exploit the features extracted in the transform domain. The iterative optimization process of the proposed algorithm is mapped into a deep unfolding network, and a Stage Information Memory Network (SIMN) is designed to address information loss between adjacent stages and within each stage. Results: Experimental results on Mayo and Piglet datasets show that the proposed model outperforms state-of-the-art techniques in both quantitative metrics and visual quality. Conclusions: The proposed network effectively removes artifacts and noise from low-dose CT images, achieving excellent reconstruction performance. [ABSTRACT FROM AUTHOR] |
| Copyright of Journal of X-Ray Science & Technology is the property of Sage Publications Inc. 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.) | |
| Βάση Δεδομένων: | Complementary Index |
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| Items | – Name: Title Label: Title Group: Ti Data: Time-frequency domain prior constrained deep unfolding network for low-dose CT reconstruction. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Zhang%2C+Xiong%22">Zhang, Xiong</searchLink><br /><searchLink fieldCode="AR" term="%22Zhang%2C+Xinbo%22">Zhang, Xinbo</searchLink><br /><searchLink fieldCode="AR" term="%22Li%2C+Xinzhong%22">Li, Xinzhong</searchLink><br /><searchLink fieldCode="AR" term="%22Ali%2C+Zulfiqur%22">Ali, Zulfiqur</searchLink><br /><searchLink fieldCode="AR" term="%22Wang%2C+Yue%22">Wang, Yue</searchLink><br /><searchLink fieldCode="AR" term="%22Shangguan%2C+Hong%22">Shangguan, Hong</searchLink><br /><searchLink fieldCode="AR" term="%22Cui%2C+Xueying%22">Cui, Xueying</searchLink> – Name: TitleSource Label: Source Group: Src Data: Journal of X-Ray Science & Technology; Sep2025, Vol. 33 Issue 5, p819-830, 12p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink><br /><searchLink fieldCode="DE" term="%22Computed+tomography%22">Computed tomography</searchLink><br /><searchLink fieldCode="DE" term="%22Iterative+methods+%28Mathematics%29%22">Iterative methods (Mathematics)</searchLink><br /><searchLink fieldCode="DE" term="%22Image+processing%22">Image processing</searchLink><br /><searchLink fieldCode="DE" term="%22Convolutional+neural+networks%22">Convolutional neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Image+enhancement+%28Imaging+systems%29%22">Image enhancement (Imaging systems)</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Background: Low-dose computed tomography (LDCT) effectively reduces the risk of malignant disease; however, reducing the radiation dose introduces additional noise and stripe artifacts in the CT imaging process. While Convolutional Neural Networks (CNN) have demonstrated performance advantages in LDCT imaging tasks, their end-to-end network architecture limits adaptability to CT reconstruction tasks, leaving room for further performance improvement. Objective: To propose a low-dose CT reconstruction network based on the iterative algorithms, incorporating an interpretable network architecture to achieve superior reconstruction performance. Methods: To better adapt to CT reconstruction tasks, we proposed an interpretable deep unfolding network leveraging time-frequency and image domain priors to fully exploit the features extracted in the transform domain. The iterative optimization process of the proposed algorithm is mapped into a deep unfolding network, and a Stage Information Memory Network (SIMN) is designed to address information loss between adjacent stages and within each stage. Results: Experimental results on Mayo and Piglet datasets show that the proposed model outperforms state-of-the-art techniques in both quantitative metrics and visual quality. Conclusions: The proposed network effectively removes artifacts and noise from low-dose CT images, achieving excellent reconstruction performance. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of Journal of X-Ray Science & Technology is the property of Sage Publications Inc. 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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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1177/08953996251319187 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 12 StartPage: 819 Subjects: – SubjectFull: Deep learning Type: general – SubjectFull: Computed tomography Type: general – SubjectFull: Iterative methods (Mathematics) Type: general – SubjectFull: Image processing Type: general – SubjectFull: Convolutional neural networks Type: general – SubjectFull: Image enhancement (Imaging systems) Type: general Titles: – TitleFull: Time-frequency domain prior constrained deep unfolding network for low-dose CT reconstruction. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Zhang, Xiong – PersonEntity: Name: NameFull: Zhang, Xinbo – PersonEntity: Name: NameFull: Li, Xinzhong – PersonEntity: Name: NameFull: Ali, Zulfiqur – PersonEntity: Name: NameFull: Wang, Yue – PersonEntity: Name: NameFull: Shangguan, Hong – PersonEntity: Name: NameFull: Cui, Xueying IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 09 Text: Sep2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 08953996 Numbering: – Type: volume Value: 33 – Type: issue Value: 5 Titles: – TitleFull: Journal of X-Ray Science & Technology Type: main |
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