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Abstract #0666

Alleviate motion artifacts in magnetic resonance imaging images using deep learning and compressed sensing   

Long Cui1, Yang Song1, Yida Wang1, Haibin Xie1, Jianqi Li1, and Guang Yang1
1Shanghai Key Laboratory of Magnetic Resonance, Department of Physics, East China Normal University, Shanghai, China

We proposed a data-driven approach to alleviate motion artifacts in Magnetic Resonance (MR) images. Firstly, MR images were acquired using a pseudo-random k-space sampling sequence. Then a convolutional network was trained to denoise MR images containing motion artifacts, before the k-space of the denoised images were compared with the raw k-space to find out k-space lines influenced by the motion. Finally, compressed sensing (CS) was applied to those unaffected lines to reconstruct the final image. Simulated experiments proved that this approach can accurately detect k-space lines influenced by motion and reconstruct images better than those reconstructed directly by CS.

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