Learning to Reconstruct Accelerated MRI Through K-space Cold Diffusion without Noise
This addresses MRI reconstruction for medical imaging applications, representing an incremental improvement by adapting cold diffusion to k-space.
The paper tackles accelerated MRI reconstruction by proposing a k-space cold diffusion model that performs degradation and restoration in k-space without Gaussian noise, showing it generates high-quality reconstruction images.
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.