activity
20162025
most citedHigh-Frequency Space Diffusion Models for Accelerated MRI

95 citations · 141 across the 32 of their papers we have counts for

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Showing 2023Show all

10 papers · 1 filter

eess.IV2023★ 1 cited

A Two-Stage Generative Model with CycleGAN and Joint Diffusion for MRI-based Brain Tumor Detection

Wenxin Wang, Zhuo-Xu Cui, Guanxun Cheng +7

Accurate detection and segmentation of brain tumors is critical for medical diagnosis. However, current supervised learning methods require extensively annotated images and the sta…

eess.IV2023★ 1 cited

Joint Diffusion: Mutual Consistency-Driven Diffusion Model for PET-MRI Co-Reconstruction

Taofeng Xie, Zhuo-Xu Cui, Chen Luo +10

Positron Emission Tomography and Magnetic Resonance Imaging (PET-MRI) systems can obtain functional and anatomical scans. PET suffers from a low signal-to-noise ratio. Meanwhile, t…

eess.SP2023★ 1 cited

Score-based Diffusion Models With Self-supervised Learning For Accelerated 3D Multi-contrast Cardiac Magnetic Resonance Imaging

Yuanyuan Liu, Zhuo-Xu Cui, Shucong Qin +6

Long scan time significantly hinders the widespread applications of three-dimensional multi-contrast cardiac magnetic resonance (3D-MC-CMR) imaging. This study aims to accelerate 3…

eess.IV2023

Matrix Completion-Informed Deep Unfolded Equilibrium Models for Self-Supervised k-Space Interpolation in MRI

Chen Luo, Huayu Wang, Taofeng Xie +4

Recently, regularization model-driven deep learning (DL) has gained significant attention due to its ability to leverage the potent representational capabilities of DL while retain…

cs.CV2023

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…

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…

cs.CV2023★ 3 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…

eess.IV2023★ 1 cited

Meta-Learning Enabled Score-Based Generative Model for 1.5T-Like Image Reconstruction from 0.5T MRI

Zhuo-Xu Cui, Congcong Liu, Chentao Cao +6

Magnetic resonance imaging (MRI) is known to have reduced signal-to-noise ratios (SNR) at lower field strengths, leading to signal degradation when producing a low-field MRI image…

cs.LG2023★ 1 cited

Synthesizing PET images from High-field and Ultra-high-field MR images Using Joint Diffusion Attention Model

Taofeng Xie, Chentao Cao, Zhuoxu Cui +14

MRI and PET are crucial diagnostic tools for brain diseases, as they provide complementary information on brain structure and function. However, PET scanning is costly and involves…

cs.CV2023

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…