activity
20232026
most citedDP-MDM: Detail-Preserving MR Reconstruction via Multiple Diffusion Models

3 citations · 3 across the 10 of their papers we have counts for

collaborators

10 papers

cs.CV2026

Diffusion-Encoding Gaussian Field for Joint k-q dMRI Reconstruction

Zhibo Chen, Yajuan Huang, Yu Guan +3

Diffusion MRI requires repeated k-space acquisitions over multiple diffusion-encoding directions, making acquisition time dependent on both spatial and angular sampling. Existing j…

cs.CV2026

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…

cs.CV2026

Back to Physics: Operator-Guided Generative Paths for SMS MRI Reconstruction

Zhibo Chen, Yu Guan, Yajuan Huang +5

Simultaneous multi-slice (SMS) imaging with in-plane undersampling enables highly accelerated MRI but yields a strongly coupled inverse problem with deterministic inter-slice inter…

cs.CV2025

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…

physics.med-ph2025

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…

eess.IV2025

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…

eess.IV2024

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…

eess.IV2024

Zero-shot Dynamic MRI Reconstruction with Global-to-local Diffusion Model

Yu Guan, Kunlong Zhang, Qi Qi +5

Diffusion models have recently demonstrated considerable advancement in the generation and reconstruction of magnetic resonance imaging (MRI) data. These models exhibit great poten…

cs.CV2024★ 3 cited

DP-MDM: Detail-Preserving MR Reconstruction via Multiple Diffusion Models

Mengxiao Geng, Jiahao Zhu, Xiaolin Zhu +3

Detail features of magnetic resonance images play a cru-cial role in accurate medical diagnosis and treatment, as they capture subtle changes that pose challenges for doc-tors when…

eess.IV2023

Correlated and Multi-frequency Diffusion Modeling for Highly Under-sampled MRI Reconstruction

Yu Guan, Chuanming Yu, Shiyu Lu +3

Most existing MRI reconstruction methods perform tar-geted reconstruction of the entire MR image without tak-ing specific tissue regions into consideration. This may fail to emphas…