  Deep neural networks have brought remarkable breakthroughs in medical image
analysis. However, due to their data-hungry nature, the modest dataset sizes in
medical imaging projects might be hindering their full potential. Generating
synthetic data provides a promising alternative, allowing to complement
training datasets and conducting medical image research at a larger scale.
Diffusion models recently have caught the attention of the computer vision
community by producing photorealistic synthetic images. In this study, we
explore using Latent Diffusion Models to generate synthetic images from
high-resolution 3D brain images. We used T1w MRI images from the UK Biobank
dataset (N=31,740) to train our models to learn about the probabilistic
distribution of brain images, conditioned on covariables, such as age, sex, and
brain structure volumes. We found that our models created realistic data, and
we could use the conditioning variables to control the data generation
effectively. Besides that, we created a synthetic dataset with 100,000 brain
images and made it openly available to the scientific community.
