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In this study, we proposed a new concept of an attention model for deep neural network based parallel imaging. We utilized g-factor maps to inform the neural network about the location containing high possibility of aliasing artifact. Also the proposed network used sensitivity maps and acquired k-space data to ensure the data consistency. Since the g-factor attention deep neural network considered both multi-channel information and spatially variant aliasing condition, our proposed network successfully removed aliasing artifacts up to factor 6 in uniform under-sampling and showed high performance when compared to conventional parallel imaging methods.