Cross-domain Super-Resolution in Medical Imaging

The use of Super-Resolution SR algorithms applied to Magnetic Resonance Images (MRIs) is increasingly common in the medical field. Increasing the resolution of images allows physicians to more easily observe image details. Over the years, several SR approaches have been tried by researchers. Among the various approaches, Diffusion Models (DMs) have been shown to perform well in the SR task. In this work, we propose the use of a Latent Diffusion Model (LDM) for the SR of medical images. Different studies have shown that LDMs improve the performance of DMs in several SR tasks. To our knowledge, LDMs have not been tested for SR of medical images such as MRIs. We therefore perform fine-tuning of an LDM on medical datasets. To evaluate the SR images generated by the LDM, we compare them to the original high-resolution images using two similarity measurements. We show that the LDM achieves better similarity values than other SR models on the same medical dataset. We also show with visual examples the advantage of applying SR using an LDM.