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.

翻译：基于深度学习的磁共振成像（MRI）重建模型近年来已取得卓越性能。最近，扩散模型在图像生成、修复、超分辨率、图像编辑等领域展现出惊人效果。作为广义扩散模型，冷扩散进一步拓展应用范围，可基于任意图像变换（如模糊、下采样等）构建模型。本文提出一种K空间冷扩散模型，该模型在K空间中执行图像退化与恢复，无需引入高斯噪声。我们与多种基于深度学习的MRI重建模型进行对比，并在知名大型开源MRI数据集上开展测试。结果表明，这种新型退化方式能够为加速MRI生成高质量重建图像。