  The generation of three-dimensional (3D) medical images has great application
potential since it takes into account the 3D anatomical structure. Two problems
prevent effective training of a 3D medical generative model: (1) 3D medical
images are expensive to acquire and annotate, resulting in an insufficient
number of training images, and (2) a large number of parameters are involved in
3D convolution.
  Methods: We propose a novel GAN model called 3D Split&Shuffle-GAN. To address
the 3D data scarcity issue, we first pre-train a two-dimensional (2D) GAN model
using abundant image slices and inflate the 2D convolution weights to improve
the initialization of the 3D GAN. Novel 3D network architectures are proposed
for both the generator and discriminator of the GAN model to significantly
reduce the number of parameters while maintaining the quality of image
generation. Several weight inflation strategies and parameter-efficient 3D
architectures are investigated.
  Results: Experiments on both heart (Stanford AIMI Coronary Calcium) and brain
(Alzheimer's Disease Neuroimaging Initiative) datasets show that our method
leads to improved 3D image generation quality (14.7 improvements on Fr\'echet
inception distance) with significantly fewer parameters (only 48.5% of the
baseline method).
  Conclusions: We built a parameter-efficient 3D medical image generation
model. Due to the efficiency and effectiveness, it has the potential to
generate high-quality 3D brain and heart images for real use cases.
