SDlM: Improved Medical Synthesis of MRI Images Through Diffusion Models

We introduce SDIM, a latent diffusion model for the synthesis of medical MRI images. In contrast to earlier iterations of Stable Diffusion, SDIM uses a bigger UNet backbone. Since SDIM uses a medical MRI image synthesis encoder, the rise in model parameters is mostly caused by additional focus frames and a bigger cross-attention context. We create various conditioning plans and use various aspect ratios to teach SDIM. Additionally, we present a refinement model that employs a post-hoc image-to-image method to enhance the visual integrity of samples produced by SDIM. We show that SDIM performs far better than earlier iterations of Stable Diffusion and produces outcomes on par with those of cutting-edge, black-box image generators. We make code and model weights available in an effort to support open research and transparency in large-scale model training and evaluation. The findings for value (MSE 0.4728, Fid 1.9061, and SSIM 0.9217) that we were promised were obtained.