  Deep learning-based MRI reconstruction models have achieved superior
performance these days. Most recently, diffusion models have shown remarkable
performance in image generation, in-painting, super-resolution, image editing
and more. As a generalized diffusion model, cold diffusion further broadens the
scope and considers models built around arbitrary image transformations such as
blurring, down-sampling, etc. In this paper, we propose a k-space cold
diffusion model that performs image degradation and restoration in k-space
without the need for Gaussian noise. We provide comparisons with multiple deep
learning-based MRI reconstruction models and perform tests on a well-known
large open-source MRI dataset. Our results show that this novel way of
performing degradation can generate high-quality reconstruction images for
accelerated MRI.
