Abstract #3540

Super-Resolution with Conditional-GAN for MR Brain Images

Alessandro Sciarra^1,2, Max Dünnwald^1,3, Hendrik Mattern², Oliver Speck^2,4,5,6, and Steffen Oeltze-Jafra^1,4

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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