A cascaded hybrid domain generative adversarial network is proposed for accelerated MRI reconstruction. A novel multi-scale feature fusion sampling layer is proposed to replace the pooling layers and upsampling layers in the U-Net k-space generator to better recover the missing samplings. The proposed method is extensively validated with low and high acceleration factors against several state-of-the-art reconstruction methods, and achieves competitive reconstruction performance.
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