Compressed sensing (CS) has been employed to accelerate magnetic resonance imaging (MRI) by sampling fewer measurements. We proposed a fusion model based on the optimization method to integrate the image and gradient-based priors into CS-MRI for better reconstruction results via convolutional neural network models. In addition, the proposed fusion model exhibited effective reconstruction performance in magnetic resonance angiography (MRA).
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