Conditional Denoising Diffusion Probabilistic Models for Inverse MR Image Recovery

Abstract

High-resolution, multi-contrast magnetic resonance imaging (MRI) protocols are required for accurate clinical diagnoses, but are limited by long scan times. Recovering high-quality, multi-contrast images from low-quality accelerated acquisitions is a promising approach to mitigate this limitation. Prior studies have demonstrated deep-learning for tasks such as contrast synthesis, image super-resolution, and image reconstruction. However, each of these tasks requires different architectures and training paradigms. Motivated by these challenges, we introduce a unified conditional denoising diffusion probabilistic model (DDPM) for inverse MR image recovery. Experiments performed on three image recovery tasks demonstrate that DDPMs achieve superior performance compared to prior state-of-the-art approaches.