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An improved low‐rank plus sparse unrolling network method for dynamic magnetic resonance imaging.

Authors :
Jiang, Ming‐feng
Chen, Yun‐jiang
Ruan, Dong‐sheng
Yuan, Zi‐han
Zhang, Ju‐cheng
Xia, Ling
Source :
Medical Physics. Nov2024, p1. 12p. 10 Illustrations.
Publication Year :
2024

Abstract

Background Purpose Methods Results Conclusions Recent advances in deep learning have sparked new research interests in dynamic magnetic resonance imaging (MRI) reconstruction. However, existing deep learning‐based approaches suffer from insufficient reconstruction efficiency and accuracy due to the lack of time correlation modeling during the reconstruction procedure.Inappropriate tensor processing steps and deep learning models may lead to not only a lack of modeling in the time dimension but also an increase in the overall size of the network. Therefore, this study aims to find suitable tensor processing methods and deep learning models to achieve better reconstruction results and a smaller network size.We propose a novel unrolling network method that enhances the reconstruction quality and reduces the parameter redundancy by introducing time correlation modeling into MRI reconstruction with low‐rank core matrix and convolutional long short‐term memory (ConvLSTM) unit.We conduct extensive experiments on AMRG Cardiac MRI dataset to evaluate our proposed approach. The results demonstrate that compared to other state‐of‐the‐art approaches, our approach achieves higher peak signal‐to‐noise ratios and structural similarity indices at different accelerator factors with significantly fewer parameters.The improved reconstruction performance demonstrates that our proposed time correlation modeling is simple and effective for accelerating MRI reconstruction. We hope our approach can serve as a reference for future research in dynamic MRI reconstruction. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
00942405
Database :
Academic Search Index
Journal :
Medical Physics
Publication Type :
Academic Journal
Accession number :
181130997
Full Text :
https://doi.org/10.1002/mp.17501