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Abstract #3480

Retrospective Artifact Correction of Pediatric MRI via Disentangled Cycle-Consistency Adversarial Networks

Siyuan Liu1, Kim-Han Thung1, Weili Lin1, Pew-Thian Yap1, and the UNC/UMN Baby Connectome Project Consortium2
1Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States, 2University of North Carolina at Chapel Hill, Chapel Hill, NC, United States

Retrospective artifact removal using supervised learning requires explicit generation of artifact-corrupted images and is impractical since generating the wide variety of potential artifacts can be challenging. Using unsupervised learning, we show how artifacts can be disentangled with remarkable efficacy from artifact-corrupted images to recover the artifact-free counterparts, without requiring explicit artifact generation.

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