Analysis of disentangled representation learning for high-resolution dynamic MRI synthesis
Claire Scavinner-Dorval, Rodolphe Bailly, Bhushan S. Borotikar, Sylvain Brochard, D. Ben Salem, Francois Waldeck Rousseau · 2024
Unpaired image synthesis is a particularly active area of research, especially in medical imaging, where paired datasets are rare. Disentangled representations are an important part of the techniques used, following those based on GAN. However, by relying on the factorization of an image into independent variation latent codes, these methods can offer greater control over the synthesis result than GANs. This work investigates the use of disentangled representation learning for high-resolution dynamic MRI synthesis.