This paper develops a deep learning based multi-contrast MR imaging method. Unlike existing methods which mainly draw prior information from the target structure or a few reference images, we design a multi-contrast convolutional neural network to draw automatic feature descriptors for describing the multi-contrast correlations and identify the nonlinear mapping with the utilization of enormous existing multi-contrast MR images as training samples. Once the network is learned, it performs as a predicator for the online multi-contrast MR imaging. Experimental results on multi-contrast in vivo dataset show that the proposed method could restore lost information from the undersampled MR images while keeping their contrasts.
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