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.

本文提出了一种k空间冷扩散模型，用于在k空间中进行图像退化和恢复，而无需高斯噪声，并通过与多种基于深度学习的MRI重建模型进行比较，在一个著名的大型开源MRI数据集上进行了测试。结果显示，这种新颖的退化方式能够生成加速MRI的高质量重建图像。

K空间冷扩散：学习重建加速MRI图像不带噪声