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Abstract #4857

Motion Correction of Magnitude MR Images using Generative Adversarial Networks

Yuan Bian1, Ye Wang2, and Stanley Reeves1

1Electrical and Computer Engineering, Auburn University, Auburn, AL, United States, 2Computer Science and Software Engineering, Auburn University, Auburn, AL, United States

Motion during MRI scan can reduce image quality due to the induced artifacts. We present a novel data-driven motion correction method for magnitude MR images using generative adversarial networks (GANs). GANs (Pix2pix model) is implemented to reduce motion artifacts and reconstruct motion-corrupted images through adversarial training between generator and discriminator to force motion-corrected image close to the reference image. The training set is made of image pairs, which consist of motionless reference images and corresponding motion-simulated images. The proposed method was validated by a simulated motion test set and a real motion (experimental) test set.

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