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

• URL: http://arxiv.org/abs/2311.10162v2
• Date: Fri, 16 Feb 2024 18:08:30 GMT
• Title: K-space Cold Diffusion: Learning to Reconstruct Accelerated MRI without Noise
• Authors: Guoyao Shen, Mengyu Li, Chad W. Farris, Stephan Anderson, Xin Zhang
• Abstract summary: We propose a k-space cold diffusion model that performs image degradation and restoration in k-space without the need for Gaussian noise. Our results show that this novel way of performing degradation can generate high-quality reconstruction images for accelerated MRI.
• Score: 2.982793366290863
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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