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20192025
most citedSRR-Net: A Super-Resolution-Involved Reconstruction Method for High Resolution MR Imaging

3 citations · 11 across the 14 of their papers we have counts for

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5 papers · 1 filter

cs.CV2025

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…

cs.CV2025

Self-supervised Deep Unrolled Model with Implicit Neural Representation Regularization for Accelerating MRI Reconstruction

Jingran Xu, Yuanyuan Liu, Yuanbiao Yang +7

Magnetic resonance imaging (MRI) is a vital clinical diagnostic tool, yet its application is limited by prolonged scan times. Accelerating MRI reconstruction addresses this issue b…

cs.CV20233 cited

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…

cs.CV20213 cited

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…

cs.CV2019

Model-based Deep Medical Imaging: the roadmap of generalizing iterative reconstruction model using deep learning

Jing Cheng, Haifeng Wang, Yanjie Zhu +10

Medical imaging is playing a more and more important role in clinics. However, there are several issues in different imaging modalities such as slow imaging speed in MRI, radiation…