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Abstract #4430

Correction of Out-of-FOV Motion Artifacts using Convolutional Neural Network Derived Prior Image

Chengyan Wang1, Yuan Wu1, Yucheng Liang2, Danni Yang2, Siwei Zhao1, and Yiping P. Du1

1Institute for Medical Imaging Technology (IMIT), School of Biomedical Engineering, Shanghai Jiao Tong university, Shanghai, China, 2Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, United States

This study presented a new motion correction algorithm with the incorporation of convolutional neural network (CNN) derived prior image to solve the out-of-FOV motion problem. A modified U-net network was developed by introducing motion parameters into the loss function. We assessed the performance of the proposed CNN-based algorithm on 1113 MPRAGE images with simulated oscillating and sudden motion trajectories. Results show that the proposed algorithm outperforms conventional TV-based algorithm with lower NMSE and higher SSIM. Besides, robust reconstruction was achieved with even 20% data missed due to the out-of-FOV motion.

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