Copertina

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

Salvato in:
Pubblicato in:arXiv.org (Dec 5, 2024), p. n/a
Autore principale: Shen, Guoyao
Altri autori: Li, Mengyu, Farris, Chad W, Anderson, Stephan, Zhang, Xin
Pubblicazione:
Cornell University Library, arXiv.org
Soggetti:
Image degradation
Magnetic resonance imaging
Random noise
Image resolution
Image quality
Image reconstruction
Deep learning
Blurring
Image processing
Accesso online:Citation/Abstract
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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.
ISSN:2331-8422
Fonte:Engineering Database