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
Every edge carries a method, confidence, and the source snippet that justified it — so bad links are debuggable.
- LinkedLinked via arxiv author · 85%Yu Guan →
“High-dimensional Embedding Prior for Noisy K-space Domain MRIReconstruction”
- LinkedLinked via arxiv author · 85%Tianjia Huang →
“High-dimensional Embedding Prior for Noisy K-space Domain MRIReconstruction”
- LinkedLinked via arxiv author · 85%Qinrong Cai →
“High-dimensional Embedding Prior for Noisy K-space Domain MRIReconstruction”
- LinkedLinked via arxiv author · 85%Qiuyun Fan →
“High-dimensional Embedding Prior for Noisy K-space Domain MRIReconstruction”
- LinkedLinked via arxiv author · 85%Dong Liang →
“High-dimensional Embedding Prior for Noisy K-space Domain MRIReconstruction”
- LinkedLinked via arxiv author · 85%Qiegen Liu →
“High-dimensional Embedding Prior for Noisy K-space Domain MRIReconstruction”
- FuzzyOverlapping authors or contributors · 62%modular/modular →
“Shared author/contributor keys: liu”