  Currently generative adversarial networks (GANs) are rarely applied to
medical images of large sizes, especially 3D volumes, due to their large
computational demand. We propose a novel multi-scale patch-based GAN approach
to generate large high resolution 2D and 3D images. Our key idea is to first
learn a low-resolution version of the image and then generate patches of
successively growing resolutions conditioned on previous scales. In a domain
translation use-case scenario, 3D thorax CTs of size 512x512x512 and thorax
X-rays of size 2048x2048 are generated and we show that, due to the constant
GPU memory demand of our method, arbitrarily large images of high resolution
can be generated. Moreover, compared to common patch-based approaches, our
multi-resolution scheme enables better image quality and prevents patch
artifacts.
