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Medical image reconstruction with multi-level deep learning denoiser and tight frame regularization

文献类型：期刊论文

作者Wu, Tingting1; Huang, Chaoyan4; Jia, Shilong2; Li, Wei1; Chan, Raymond3; Zeng, Tieyong4; Zhou, S. Kevin5,6
刊名APPLIED MATHEMATICS AND COMPUTATION
出版日期2024-09-15
卷号477页码:19
关键词Medical image reconstruction Multi-level wavelet convolutional neural network Tight frame Proximal alternating minimization Magnetic resonance imaging Positron emission tomography
ISSN号0096-3003
DOI10.1016/j.amc.2024.128795
英文摘要As a fundamental task, medical image reconstruction has attracted growing attention in clinical diagnosis. Aiming at promising performance, it is critical to deeply understand and effectively design advanced model for image reconstruction. Indeed, one possible solution is to integrate the deep learning methods with the variational approaches to absorb benefits from both parts. In this paper, to protect more details and a better balance between the computational burden and the numerical performance, we carefully choose the multi -level wavelet convolutional neural network (MWCNN) for this issue. As the tight frame regularizer has the capability of maintaining edge information in image, we combine the MWCNN with the tight frame regularizer to reconstruct images. The proposed model can be solved by the celebrated proximal alternating minimization (PAM) algorithm. Furthermore, our method is a noise -adaptive framework as it can also handle real -world images. To prove the robustness of our strategy, we address two important medical image reconstruction tasks: Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET). Extensive numerical experiments show clearly that our approach achieves better performance over several state-of-the-art methods.
资助项目1311 Talent Plan of NUPT[61971234] ; 1311 Talent Plan of NUPT[11671002] ; 1311 Talent Plan of NUPT[12126340] ; 1311 Talent Plan of NUPT[12126304] ; QingLan Project for Colleges and Univer-sities of Jiangsu Province ; STITP[XZD2020122] ; Nanjing University of Posts and Telecommunications Project ; [NY223008]
WOS研究方向Mathematics
语种英语
WOS记录号WOS:001241682900001
出版者ELSEVIER SCIENCE INC
源URL[http://119.78.100.204/handle/2XEOYT63/40038]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Zeng, Tieyong
作者单位1.Nanjing Univ Posts & Telecommun, Sch Sci, Nanjing, Peoples R China
2.Nanjing Univ Posts & Telecommun, Sch Comp Sci, Nanjing, Peoples R China
3.City Univ Hong Kong, Dept Math, Hong Kong, Peoples R China
4.Chinese Univ Hong Kong, Dept Math, Shatin, Hong Kong, Peoples R China
5.Chinese Acad Sci, Key Lab Intelligent Informat Proc CAS, MIRACLE Grp, ICT, Beijing 100190, Peoples R China
6.Univ Chinese Acad Sci UCAS, Beijing 100049, Peoples R China
推荐引用方式
GB/T 7714		 Wu, Tingting,Huang, Chaoyan,Jia, Shilong,et al. Medical image reconstruction with multi-level deep learning denoiser and tight frame regularization[J]. APPLIED MATHEMATICS AND COMPUTATION,2024,477:19.
APA Wu, Tingting.,Huang, Chaoyan.,Jia, Shilong.,Li, Wei.,Chan, Raymond.,...&Zhou, S. Kevin.(2024).Medical image reconstruction with multi-level deep learning denoiser and tight frame regularization.APPLIED MATHEMATICS AND COMPUTATION,477,19.
MLA Wu, Tingting,et al."Medical image reconstruction with multi-level deep learning denoiser and tight frame regularization".APPLIED MATHEMATICS AND COMPUTATION 477(2024):19.

入库方式： OAI收割

来源：计算技术研究所

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