  Medical image processing has been highlighted as an area where deep
learning-based models have the greatest potential. However, in the medical
field in particular, problems of data availability and privacy are hampering
research progress and thus rapid implementation in clinical routine. The
generation of synthetic data not only ensures privacy, but also allows to
\textit{draw} new patients with specific characteristics, enabling the
development of data-driven models on a much larger scale. This work
demonstrates that three-dimensional generative adversarial networks (GANs) can
be efficiently trained to generate high-resolution medical volumes with finely
detailed voxel-based architectures. In addition, GAN inversion is successfully
implemented for the three-dimensional setting and used for extensive research
on model interpretability and applications such as image morphing, attribute
editing and style mixing. The results are comprehensively validated on a
database of three-dimensional HR-pQCT instances representing the bone
micro-architecture of the distal radius.
