Abstract
The Unet is attempted to be slimmed for compressed-sensing cardiac cine magnetic resonance imaging. Despite the excellent performance of the U-net, its heavy structure and long training time restrict its applications in an environment with limited computational resources. We slimmed the U-net by changing the multiple convolutions to single convolution and the transpose convolution to upsampling and by adopting fewer layers, without performance degradation. The number of trainable weights and training time of the slimmed network was reduced by 87.9% and 48.1%, respectively. The proposed network showed improved performance with a 1.45% reduction in the normalised mean square error.
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