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Abstract #3977

Super resolution reconstruction in MRI by deep convolutional neural networks

zhengchao dong1 and hong Wang2

11. Columbia University, New York, NY, United States 2. New York State Psychiatric institute, New York, NY, United States, NY, United States, 21. Columbia University, New York, NY, United States 2. School of Science,Tianjin University, Tianjin, People's Republic of China

The sparse-representation-based super resolution is an efficient learning-based method. This method involves two key steps. One is to learn two dictionaries for low/high-resolution image patches, and the other is to learn a mapping between low resolution example patches and their corresponding high resolution patches from massive external images. We presented a super resolution method for MRI from reduced k-space acquisition sequences via deep convolutional neural networks. The proposed method directly learns an end-to-end mapping between the low/high-resolution images.Our proposed method is tested on the OpenfMRI database. It significantly outperforms the zero-filled reconstruction and an existing learning-based MRI SR method.

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