Recently, a new one-minute multi-contrast echo-planar imaging (EPI) based sequence (EPIMix) is proposed for brain magnetic resonance imaging (MRI). Despite the ultra-fast acquisition speed, EPIMix images suffer from lower signal-to-noise ratio (SNR) and resolution than standard scans. In this study, we tested whether an unsupervised deep learning framework could improve the image quality of EPIMix exams. We evaluated the proposed network on T2 and T2 FLAIR images and achieved promising qualitative results. The results suggest that deep learning could enable high image quality for ultra-fast EPIMix exams, which could have great clinical utility especially for patients with acute diseases.
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