High-dimensional Embedding Prior for Noisy K-space Domain MRIReconstruction
Magnetic resonance imaging (MRI) reconstruction under realistic acquisition conditions can be fundamentally viewed as estimating the underlying k-space distribution from incomplete and noise-corrupted measurements. While diffusion models have recently shown strong potential as generative prior for inverse problems,existingapproachesstruggletohandlenoisyreconstruction settings, especially when operating directly in k-space domain. In this work, we propose a unified high-dimensional k-space reconstruction framework tailored for noisy inverse problems, whichenhancesdiffusion-based solversthroughr
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Paper → model → repo connections mined from source citations (Tier-1 exact match).
Why these links exist
- Linked via arxiv authorYu Guan →
High-dimensional Embedding Prior for Noisy K-space Domain MRIReconstruction
- Linked via arxiv authorTianjia Huang →
High-dimensional Embedding Prior for Noisy K-space Domain MRIReconstruction
- Linked via arxiv authorQinrong Cai →
High-dimensional Embedding Prior for Noisy K-space Domain MRIReconstruction
- Linked via arxiv authorQiuyun Fan →
High-dimensional Embedding Prior for Noisy K-space Domain MRIReconstruction
- Linked via arxiv authorDong Liang →
High-dimensional Embedding Prior for Noisy K-space Domain MRIReconstruction
- Linked via arxiv authorQiegen Liu →
High-dimensional Embedding Prior for Noisy K-space Domain MRIReconstruction