7 papers
K-space Gaussian Representation for Parallel MRI
Yu Guan, Mingyu Hu, Jiale Hu +3
Accelerated magnetic resonance imaging (MRI) aims to recover the k-space signal from acquired measurements, where accurate estimation of missing samples is essential for high-fidel…
High-dimensional Embedding Prior for Noisy K-space Domain MRIReconstruction
Yu Guan, Tianjia Huang, Qinrong Cai +3
Magnetic resonance imaging (MRI) reconstruction under realistic acquisition conditions can be fundamentally viewed as estimating the underlying k-space distribution from incomplete…
K-Syn: K-space Data Synthesis in Ultra Low-data Regimes
Guan Yu, Zhang Jianhua, Liang Dong +1
Owing to the inherently dynamic and complex characteristics of cardiac magnetic resonance (CMR) imaging, high-quality and diverse k-space data are rarely available in practice, whi…
Diffusion-Assisted Frequency Attention Model for Whole-body Low-field MRI Reconstruction
Xin Xie, Yu Guan, Zhuoxu Cui +2
By integrating the generative strengths of diffusion models with the representation capabilities of frequency-domain attention, DFAM effectively enhances reconstruction performance…
Adaptive Mask-guided K-space Diffusion for Accelerated MRI Reconstruction
Qinrong Cai, Yu Guan, Zhibo Chen +3
As the deep learning revolution marches on, masked modeling has emerged as a distinctive approach that involves predicting parts of the original data that are proportionally masked…
Sub-DM:Subspace Diffusion Model with Orthogonal Decomposition for MRI Reconstruction
Yu Guan, Qinrong Cai, Wei Li +3
Diffusion model-based approaches recently achieved re-markable success in MRI reconstruction, but integration into clinical routine remains challenging due to its time-consuming co…