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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