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.

How to access this content:

For one year after publication, abstracts and videos are only open to registrants of this annual meeting. Registrants should use their existing login information. Non-registrant access can be purchased via the ISMRM E-Library.

After one year, current ISMRM & ISMRT members get free access to both the abstracts and videos. Non-members and non-registrants must purchase access via the ISMRM E-Library.

After two years, the meeting proceedings (abstracts) are opened to the public and require no login information. Videos remain behind password for access by members, registrants and E-Library customers.

Click here for more information on becoming a member.