Buchumschlag

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

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Veröffentlicht in:arXiv.org (Dec 5, 2024), p. n/a
1. Verfasser: Shen, Guoyao
Weitere Verfasser: Li, Mengyu, Farris, Chad W, Anderson, Stephan, Zhang, Xin
Veröffentlicht:
Cornell University Library, arXiv.org
Schlagworte:
Image degradation
Magnetic resonance imaging
Random noise
Image resolution
Image quality
Image reconstruction
Deep learning
Blurring
Image processing
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022 |a 2331-8422 
035 |a 2894057887 
045 0 |b d20241205 
100 1 |a Shen, Guoyao 
245 1 |a Learning to Reconstruct Accelerated MRI Through K-space Cold Diffusion without Noise 
260 |b Cornell University Library, arXiv.org  |c Dec 5, 2024 
513 |a Working Paper 
520 3 |a 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. 
653 |a Image degradation 
653 |a Magnetic resonance imaging 
653 |a Random noise 
653 |a Image resolution 
653 |a Image quality 
653 |a Image reconstruction 
653 |a Deep learning 
653 |a Blurring 
653 |a Image processing 
700 1 |a Li, Mengyu 
700 1 |a Farris, Chad W 
700 1 |a Anderson, Stephan 
700 1 |a Zhang, Xin 
773 0 |t arXiv.org  |g (Dec 5, 2024), p. n/a 
786 0 |d ProQuest  |t Engineering Database 
856 4 1 |3 Citation/Abstract  |u https://www.proquest.com/docview/2894057887/abstract/embedded/6A8EOT78XXH2IG52?source=fedsrch 
856 4 0 |3 Full text outside of ProQuest  |u http://arxiv.org/abs/2311.10162