Web: http://arxiv.org/abs/2206.07364

June 16, 2022, 1:13 a.m. | Yan Jiangpeng, Yu Chenghui, Chen Hanbo, Xu Zhe, Huang Junzhou, Li Xiu, Yao Jianhua

cs.CV updates on arXiv.org arxiv.org

Recently, deep neural networks have greatly advanced undersampled Magnetic
Resonance Image (MRI) reconstruction, wherein most studies follow the
one-anatomy-one-network fashion, i.e., each expert network is trained and
evaluated for a specific anatomy. Apart from inefficiency in training multiple
independent models, such convention ignores the shared de-aliasing knowledge
across various anatomies which can benefit each other. To explore the shared
knowledge, one naive way is to combine all the data from various anatomies to
train an all-round network. Unfortunately, despite the …

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