3 citations · 16 across the 22 of their papers we have counts for
7 papers · 1 filter
Flow-Guided Implicit Neural Representation for Motion-Aware Dynamic MRI Reconstruction
Baoqing Li, Yuanyuan Liu, Congcong Liu +6
Dynamic magnetic resonance imaging (dMRI) captures temporally-resolved anatomy but is often challenged by limited sampling and motion-induced artifacts. Conventional motion-compens…
Joint PET-MRI Reconstruction with Diffusion Stochastic Differential Model
Taofeng Xie, Zhuoxu Cui, Congcong Liu +9
PET suffers from a low signal-to-noise ratio. Meanwhile, the k-space data acquisition process in MRI is time-consuming by PET-MRI systems. We aim to accelerate MRI and improve PET…
Convex Latent-Optimized Adversarial Regularizers for Imaging Inverse Problems
Huayu Wang, Chen Luo, Taofeng Xie +4
Recently, data-driven techniques have demonstrated remarkable effectiveness in addressing challenges related to MR imaging inverse problems. However, these methods still exhibit ce…
Physics-Informed DeepMRI: Bridging the Gap from Heat Diffusion to k-Space Interpolation
Zhuo-Xu Cui, Congcong Liu, Xiaohong Fan +11
In the field of parallel imaging (PI), alongside image-domain regularization methods, substantial research has been dedicated to exploring -space interpolation. However, the int…
SPIRiT-Diffusion: Self-Consistency Driven Diffusion Model for Accelerated MRI
Zhuo-Xu Cui, Chentao Cao, Yue Wang +6
Diffusion models have emerged as a leading methodology for image generation and have proven successful in the realm of magnetic resonance imaging (MRI) reconstruction. However, exi…
SRR-Net: A Super-Resolution-Involved Reconstruction Method for High Resolution MR Imaging
Wenqi Huang, Sen Jia, Ziwen Ke +4
Improving the image resolution and acquisition speed of magnetic resonance imaging (MRI) is a challenging problem. There are mainly two strategies dealing with the speed-resolution…