Learning to Reconstruct Accelerated MRI Through K-space Cold Diffusion without Noise
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866913598229446656 |
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| author | Shen, Guoyao Li, Mengyu Farris, Chad W. Anderson, Stephan Zhang, Xin |
| author_facet | Shen, Guoyao Li, Mengyu Farris, Chad W. Anderson, Stephan Zhang, Xin |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2311_10162 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Learning to Reconstruct Accelerated MRI Through K-space Cold Diffusion without Noise Shen, Guoyao Li, Mengyu Farris, Chad W. Anderson, Stephan Zhang, Xin Image and Video Processing Computer Vision and Pattern Recognition Machine Learning Medical Physics 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. |
| title | Learning to Reconstruct Accelerated MRI Through K-space Cold Diffusion without Noise |
| topic | Image and Video Processing Computer Vision and Pattern Recognition Machine Learning Medical Physics |
| url | https://arxiv.org/abs/2311.10162 |