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Abstract #3370

Evaluation of Deep Learning Techniques for Motion Artifacts Removal

Alessandro Sciarra1,2, Soumick Chatterjee2,3, Max D├╝nnwald1,4, Oliver Speck2,5,6,7, and Steffen Oeltze-Jafra1,5
1MedDigit, Department of Neurology, Medical Faculty, Otto von Guericke University, Magdeburg, Germany, 2BMMR, Biomedical Magnetic Resonance, Otto von Guericke University, Magdeburg, Germany, 3Data & Knowledge Engineering Group, Otto von Guericke University, Magdeburg, Germany, 4Faculty of Computer Science, Otto von Guericke University, Magdeburg, Germany, 5Center for Behavioral Brain Sciences (CBBS), Magdeburg, Germany, 6German Center for Neurodegenerative Disease, Magdeburg, Germany, 7Leibniz Institute for Neurobiology, Magdeburg, Germany

Removing motion artifacts in MR images remains a challenging task. In this work, we employed 2 convolutional neural networks, a conditional generative adversarial network (c-GAN), also known as pix2pix, as well as a network based on the residual network (ResNet) architecture, to remove synthetic motion artifacts for phantom images and T1-w brain images. The corrected images were compared with the ground-truth ones in order to assess the performance of the chosen neural networks quantitatively and qualitatively.

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