Learning to Reconstruct Accelerated MRI Through K-space Cold Diffusion without Noise

TL;DR

This paper introduces a novel k-space cold diffusion model for accelerated MRI reconstruction that operates without Gaussian noise, demonstrating high-quality results on large open-source datasets.

Contribution

It presents a new diffusion-based MRI reconstruction method that performs degradation and restoration directly in k-space without noise, expanding the application of diffusion models.

Findings

Outperforms existing deep learning MRI reconstruction models

Generates high-quality images for accelerated MRI

Validated on large open-source MRI dataset

Abstract

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.