Abstract #3370

Evaluation of Deep Learning Techniques for Motion Artifacts Removal

Alessandro Sciarra^1,2, Soumick Chatterjee^2,3, Max Dünnwald^1,4, Oliver Speck^2,5,6,7, and Steffen Oeltze-Jafra^1,5

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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