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Abstract #3540

Super-Resolution with Conditional-GAN for MR Brain Images 

Alessandro Sciarra1,2, Max Dünnwald1,3, Hendrik Mattern2, Oliver Speck2,4,5,6, and Steffen Oeltze-Jafra1,4
1MedDigit, Department of Neurology, Medical Faculty, Otto von Guericke University, Magdeburg, Germany, 2BMMR, Biomedical Magnetic Resonance, Otto von Guericke University, Magdeburg, Germany, 3Faculty of Computer Science, Otto von Guericke University, Magdeburg, Germany, 4Center for Behavioral Brain Sciences (CBBS), Magdeburg, Germany, 5German Center for Neurodegenerative Disease, Magdeburg, Germany, 6Leibniz Institute for Neurobiology, Magdeburg, Germany

In clinical routine acquisitions, the resolution in the slice direction is often worse than the in-plane resolution. Super-resolution techniques can help to retrieve the lack of information. Employing a conditional generative adversarial network (c-GAN), known as pix2pix for T1-w brain images, we were able to reconstruct downsampled images till 10-fold. The neural network was compared with the traditional bivariate interpolation method, and the results show that pix2pix is a valid alternative.

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