期刊
MAGNETIC RESONANCE IMAGING
卷 68, 期 -, 页码 136-147出版社
ELSEVIER SCIENCE INC
DOI: 10.1016/j.mri.2020.02.002
关键词
Deep learning; Convolutional neural network; Fast MR imaging; Prior knowledge; Parallel imaging
资金
- National Natural Science Foundation of China [61871371, 81830056, U1805261]
- Key-Area Research and Development Program of GuangDong Province [2018B010109009]
- Science and Technology Planning Project of Guangdong Province [2017B020227012]
- Basic Research Program of Shenzhen [JCYJ20180507182400762]
- Youth Innovation Promotion Association of Chinese Academy of Sciences [2019351]
- Chinese Academy of Sciences Engineering Laboratory for Medical Imaging Technology and Equipment [KFJ-PTXM-012]
This paper proposes a multi-channel image reconstruction method, named DeepcomplexMRI, to accelerate parallel MR imaging with residual complex convolutional neural network. Different from most existing works which rely on the utilization of the coil sensitivities or prior information of predefined transforms, DeepcomplexMRI takes advantage of the availability of a large number of existing multi-channel groudtruth images and uses them as target data to train the deep residual convolutional neural network offline. In particular, a complex convolutional network is proposed to take into account the correlation between the real and imaginary parts of MR images. In addition, the k-space data consistency is further enforced repeatedly in between layers of the network. The evaluations on in vivo datasets show that the proposed method has the capability to recover the desired multi-channel images. Its comparison with state-of-the-art methods also demonstrates that the proposed method can reconstruct the desired MR images more accurately.
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