A Preliminary Study on Augmenting Neuroimaging data using a Diffusion Model

This preliminary study explores the use of diffusion models for brain imaging generation to address the limitations of small datasets in rare neurodegenerative conditions. Our goal is to improve model robustness by generating realistic variations in medical images. Data scarcity is the main issue for the application of deep learning techniques in neurodegeneration. In the last decade, diffusion models have tried to address this problem as a novel generative technique widely applied for image and video generation. A diffusion model, known for capturing complex data distributions, was trained on a multicenter dataset of Structural Magnetic Resonance Images of healthy subjects to generate a high-quality synthetic dataset. Our results show that the Maximum Mean Discrepancy between two distributions is 0.036, thus indicating that the two distributions are quite similar. However, other metrics such as the Frechet Inception Distance and the Multiscale Structural Similarity Index Measure achieve suboptimal results. Although far from model optimization, these preliminary results demonstrate that diffusion models can be a valid tool to generate high-quality brain imaging data.