MR image synthesis using Riemannian geometry constrained in VAE

Deep generative models are used to learn nonlinear data distributions across a set of latent variables and a generator function which maps the latent variables to the input space. In this paper, we propose a deep generative model based on variational auto-encoders (VAE) with Riemannian geometry for synthetizing MRI images. Riemannian geometry is used as a constraint to extract the most relevant and structuring features in latent space. This approach is applied on Brats20 data. Quantitative comparisons between a classical VAE and our method are carried out to evaluate the quality of the generated images. The results demonstrate the superiority of our approach.