  Learning based single image super resolution (SISR) task is well investigated
in 2D images. However, SISR for 3D Magnetics Resonance Images (MRI) is more
challenging compared to 2D, mainly due to the increased number of neural
network parameters, the larger memory requirement and the limited amount of
available training data. Current SISR methods for 3D volumetric images are
based on Generative Adversarial Networks (GANs), especially Wasserstein GANs
due to their training stability. Other common architectures in the 2D domain,
e.g. transformer models, require large amounts of training data and are
therefore not suitable for the limited 3D data. However, Wasserstein GANs can
be problematic because they may not converge to a global optimum and thus
produce blurry results. Here, we propose a new method for 3D SR based on the
GAN framework. Specifically, we use instance noise to balance the GAN training.
Furthermore, we use a relativistic GAN loss function and an updating feature
extractor during the training process. We show that our method produces highly
accurate results. We also show that we need very few training samples. In
particular, we need less than 30 samples instead of thousands of training
samples that are typically required in previous studies. Finally, we show
improved out-of-sample results produced by our model.
