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Abstract #4885

Enhance One-minute EPIMix Brain MRI Exams with Unsupervised Cycle-Consistent Generative Adversarial Network

Jiang Liu1, Enhao Gong2,3, Stefan Skare4, and Greg Zaharchuk2

1Tsinghua University, Beijing, China, 2Stanford University, Stanford, CA, United States, 3Subtle Medical Inc., Menlo Park, CA, United States, 4Karolinska Institutet, Stockholm, Sweden

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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