EPImix is a one-minute full brain magnetic resonance exam using a multicontrast echo-planar imaging (EPI) sequence, which can generate six contrasts at a time. However, the low resolution and signal-to-noise ratio impeded its application. And EPI distortion makes it harder to improve the quality of imaging. In this study, we applied topup for distortion correction and propose a supervised deep learning model to enhance the EPImix images. We test the GRE, T2, T2FLAIR images on network and analyze the peak signal to noise ratio and structural similarity results. The results suggest that the proposed method can effectively enhance EPImix images.