  As deep learning is showing unprecedented success in medical image analysis
tasks, the lack of sufficient medical data is emerging as a critical problem.
While recent attempts to solve the limited data problem using Generative
Adversarial Networks (GAN) have been successful in generating realistic images
with diversity, most of them are based on image-to-image translation and thus
require extensive datasets from different domains. Here, we propose a novel
model that can successfully generate 3D brain MRI data from random vectors by
learning the data distribution. Our 3D GAN model solves both image blurriness
and mode collapse problems by leveraging alpha-GAN that combines the advantages
of Variational Auto-Encoder (VAE) and GAN with an additional code discriminator
network. We also use the Wasserstein GAN with Gradient Penalty (WGAN-GP) loss
to lower the training instability. To demonstrate the effectiveness of our
model, we generate new images of normal brain MRI and show that our model
outperforms baseline models in both quantitative and qualitative measurements.
We also train the model to synthesize brain disorder MRI data to demonstrate
the wide applicability of our model. Our results suggest that the proposed
model can successfully generate various types and modalities of 3D whole brain
volumes from a small set of training data.
