2 citations · 5 across the 12 of their papers we have counts for
12 papers
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…
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…
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…
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…
Synthetic CT Generation via Variant Invertible Network for All-digital Brain PET Attenuation Correction
Yu Guan, Bohui Shen, Xinchong Shi +3
Attenuation correction (AC) is essential for the generation of artifact-free and quantitatively accurate positron emission tomography (PET) images. However, AC of PET faces challen…
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…
Universal Generative Modeling in Dual-domain for Dynamic MR Imaging
Chuanming Yu, Yu Guan, Ziwen Ke +2
Dynamic magnetic resonance image reconstruction from incomplete k-space data has generated great research interest due to its capability to reduce scan time. Never-theless, the rec…
K-space and Image Domain Collaborative Energy based Model for Parallel MRI Reconstruction
Zongjiang Tu, Chen Jiang, Yu Guan +4
Decreasing magnetic resonance (MR) image acquisition times can potentially make MR examinations more accessible. Prior arts including the deep learning models have been devoted to…
MRI Reconstruction Using Deep Energy-Based Model
Yu Guan, Zongjiang Tu, Shanshan Wang +3
Purpose: Although recent deep energy-based generative models (EBMs) have shown encouraging results in many image generation tasks, how to take advantage of the self-adversarial cog…
Iterative Reconstruction for Low-Dose CT using Deep Gradient Priors of Generative Model
Zhuonan He, Yikun Zhang, Yu Guan +4
Dose reduction in computed tomography (CT) is essential for decreasing radiation risk in clinical applications. Iterative reconstruction is one of the most promising ways to compen…