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Abstract #1979

A Modified Generative Adversarial Network using Spatial and Channel-wise Attention for Compressed Sensing MRI Reconstruction

Guangyuan Li1, Chengyan Wang2, Weibo Chen3, and Jun Lyu1
1School of Computer and Control Engineering, Yantai University, Yantai, China, 2Human Phenome Institute, Fudan University, Shanghai, China, 3Philips Healthcare, Shanghai, China

At present, many deep learning-based methods have been proposed to solve the problems of traditional CS-MRI, but the reconstruction effect under highly under-sampling has not been well resolved. We proposed a modified GAN architecture for accelerating CS-MRI reconstruction, namely RSCA-GAN. The generator in the proposed architecture is composed of two residual U-net block, in which we added spatial and channel-wise attention (SCA). Each encoding-decoding block is composed of two residual blocks with short skip connections. SCA are added to the decoding block and residual block.

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