  Recent advances in computer vision have shown promising results in image
generation. Diffusion probabilistic models in particular have generated
realistic images from textual input, as demonstrated by DALL-E 2, Imagen and
Stable Diffusion. However, their use in medicine, where image data typically
comprises three-dimensional volumes, has not been systematically evaluated.
Synthetic images may play a crucial role in privacy preserving artificial
intelligence and can also be used to augment small datasets. Here we show that
diffusion probabilistic models can synthesize high quality medical imaging
data, which we show for Magnetic Resonance Images (MRI) and Computed Tomography
(CT) images. We provide quantitative measurements of their performance through
a reader study with two medical experts who rated the quality of the
synthesized images in three categories: Realistic image appearance, anatomical
correctness and consistency between slices. Furthermore, we demonstrate that
synthetic images can be used in a self-supervised pre-training and improve the
performance of breast segmentation models when data is scarce (dice score 0.91
vs. 0.95 without vs. with synthetic data). The code is publicly available on
GitHub: https://github.com/FirasGit/medicaldiffusion.
