Brain Imaging Generation with Latent Diffusion Models
This addresses data scarcity for medical imaging researchers, enabling larger-scale studies, though it is incremental as it applies an existing method to a new domain.
The study tackled the problem of limited dataset sizes in medical imaging by generating synthetic high-resolution 3D brain images using Latent Diffusion Models, resulting in realistic data controlled by variables like age and sex, and producing a publicly available synthetic dataset of 100,000 images.
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.