  In medical imaging, a general problem is that it is costly and time consuming
to collect high quality data from healthy and diseased subjects. Generative
adversarial networks (GANs) is a deep learning method that has been developed
for synthesizing data. GANs can thereby be used to generate more realistic
training data, to improve classification performance of machine learning
algorithms. Another application of GANs is image-to-image translations, e.g.
generating magnetic resonance (MR) images from computed tomography (CT) images,
which can be used to obtain multimodal datasets from a single modality. Here,
we evaluate two unsupervised GAN models (CycleGAN and UNIT) for image-to-image
translation of T1- and T2-weighted MR images, by comparing generated synthetic
MR images to ground truth images. We also evaluate two supervised models; a
modification of CycleGAN and a pure generator model. A small perceptual study
was also performed to evaluate how visually realistic the synthesized images
are. It is shown that the implemented GAN models can synthesize visually
realistic MR images (incorrectly labeled as real by a human). It is also shown
that models producing more visually realistic synthetic images not necessarily
have better quantitative error measurements, when compared to ground truth
data. Code is available at https://github.com/simontomaskarlsson/GAN-MRI
