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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