文献类型：期刊文献

英文题名：Integrating Gaussian mixture model with adjacent spatial adaptive transformer multi-stage network for magnetic resonance image reconstruction

作者：Hou, Tong[1];Zhu, Hongqing[1];Liu, Jiahao[1];Chen, Ning[1];Yan, Jiawei[1];Huang, Bingcang[2];Lu, Weiping[2];Yang, Suyi[3];Wang, Ying[4]

机构：[1]East China Univ Sci & Technol, Sch Informat Sci & Engn, Shanghai 200237, Peoples R China;[2]Gongli Hosp Shanghai Pudong New Area, Dept Radiol, Shanghai 200135, Peoples R China;[3]UCL, Fac Engn Sci, London WC1E 6BT, England;[4]Gongli Hosp Shanghai Pudong New Area, Shanghai Hlth Commiss Key Lab Artificial Intellige, Sino French Cooperat Cent Lab, Key Lab Artificial Intelligence AI Based Managemen, Shanghai 200135, Peoples R China

年份：2025

卷号：104

外文期刊名：BIOMEDICAL SIGNAL PROCESSING AND CONTROL

收录：;EI(收录号：20250517774941);WOS:【SCI-EXPANDED(收录号:WOS:001420509800001)】；

基金：The authors would like to thank the anonymous reviewers and the associate editor for their insightful comments that significantly improved the quality of this paper. This work was supported by the National Natural Science Foundation of China under Grant 61872143, 82372029, 61771196. Discipline Construction of Pudong New Area Health Commission (PWGw2020-01, PWZxk2022-03) . Joint Research Project of Pudong New Area Health and Family Planning Commission (PW2021D-14) .

语种：英文

外文关键词：MRI reconstruction; Deep unfolding network; Iterative shrinkage threshold algorithm; Gaussian mixture models; Transformer

摘要：Accurate and fast magnetic resonance imaging (MRI) reconstruction using undersampled data is crucial for practical applications. This paper proposes a novel multi-stage network, termed IGT-Net, which integrates the Gaussian mixture model (GMM) with an adjacent spatial adaptive transformer (ASAT). Specifically, IGT-Net consists of a compressive sensing sampling initialization module (CSS-IM) and a reconstruction group. The CSS-IM is designed to equivalently mimic the sampling process and transmit MRI images of different sampling rates to the reconstruction group. The reconstruction group consists of the proposed transformer-based iterative shrinkage threshold algorithm (TrISTA) and transformer-based Gaussian mixture model (TrGMM), which reconstruct clear MRI images more efficiently. In TrISTA, the dynamic gradient descent module (DGDM) achieves fast and stable convergence of results throughout the iteration process. The proposed transformer- based proximal mapping module (TPMM) utilizes a designed transformer to fully integrate adjacent spatial features for MRI reconstruction, thereby avoiding unclear results that may arise from neglecting adjacent stage information. Meanwhile, TrGMM incorporates both GMM and ASAT to leverage maximum likelihood estimation for weight updates and artifact removal. Additionally, a plug-and-play ASAT is designed to effectively extract adjacent spatial features. Experiments conducted on two public datasets demonstrate that IGT-Net outperforms state-of-the-art methods and significantly improves the quality of reconstructed images.