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In this work we propose an adversarial learning approach to generate highresolution MRI scans from low resolution images. The architecture, based on theSRGAN model, adopts 3D convolutions to exploit volumetric information. For thediscriminator, the adversarial loss uses least squares in order to stabilizethe training. For the generator, the loss function is a combination of a leastsquares adversarial loss and a content term based on mean square error andimage gradients in order to improve the quality of the generated images. Weexplore different solutions for the upsampling phase. We present promisingresults that improve classical interpolation, showing the potential of theapproach for 3D medical imaging super-resolution. Source code available athttps://github.com/imatge-upc/3D-GAN-superresolution

Brain MRI super-resolution using 3D generative adversarial networks