Journal
JOURNAL OF MAGNETIC RESONANCE
Volume 318, Issue -, Pages -Publisher
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.jmr.2020.106790
Keywords
Magnetic resonance imaging; Image reconstruction; Deep learning; Sparse learning; Network interpretability
Funding
- National Key R&D Program of China, China [2017YFC0108703]
- National Natural Science Foundation of China [61971361, 61871341, 61811530021]
- Natural Science Foundation of Fujian Province of China [2018J06018]
- Fundamental Research Funds for the Central Universities [20720180056]
- Xiamen University Nanqiang Outstanding Talents Programme
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Magnetic resonance imaging has been widely applied in clinical diagnosis. However, it is limited by its long data acquisition time. Although the imaging can be accelerated by sparse sampling and parallel imaging, achieving promising reconstructed images with a fast computation speed remains a challenge. Recently, deep learning methods have attracted a lot of attention for encouraging reconstruction results, but they are lack of proper interpretability for neural networks. In this work, in order to enable high-quality image reconstruction for the parallel magnetic resonance imaging, we design the network structure from the perspective of sparse iterative reconstruction and enhance it with the residual structure. Experimental results on a public knee dataset indicate that, as compared with the state-of-the-art deep learning-based and optimization-based methods, the proposed network achieves lower error in reconstruction and is more robust under different samplings. (C) 2020 Elsevier Inc. All rights reserved.
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