To demonstrate the feasibility of combining parallel imaging (PI) with the generative adversarial network (GAN) for accelerated multi-channel MRI reconstruction. In our proposed PIC-GAN framework, we used a progressive refinement method in both frequency and image domains, which can not only help to stabilize the optimization of the network, but also make full use of the complementarity of the two domains. More specifically, the loss function in the image domain ensures to reduce aliasing artifacts between the reconstructed images and their corresponding ground truth. This enables the model to ensure high-fidelity reconstructions can be obtained even at high acceleration factors.
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