Abstract #4616

Automatic Quality Assessment of Pediatric MRI via Nonlocal Residual Neural Networks

Siyuan Liu¹, Kim-Han Thung¹, Weili Lin¹, Pew-Thian Yap¹, Dinggang Shen¹, and UNC/UMN Baby Connectome Project Consortium²

¹Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States, ²Univerisity of North Carolina at Chapel Hill, Chapel Hill, NC, United States

Manual MRI quality assessment is time-consuming, subjective, and error-prone. We show that image quality of contrast-varying pediatric MR images can be automatically assessed using deep learning with near-human accuracy.

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