1 code implementation • 6 Nov 2023 • Wenxin Wang, Zhuo-Xu Cui, Guanxun Cheng, Chentao Cao, Xi Xu, Ziwei Liu, Haifeng Wang, Yulong Qi, Dong Liang, Yanjie Zhu
However, current supervised learning methods require extensively annotated images and the state-of-the-art generative models used in unsupervised methods often have limitations in covering the whole data distribution.
no code implementations • 30 Aug 2023 • Zhuo-Xu Cui, Congcong Liu, Xiaohong Fan, Chentao Cao, Jing Cheng, Qingyong Zhu, Yuanyuan Liu, Sen Jia, Yihang Zhou, Haifeng Wang, Yanjie Zhu, Jianping Zhang, Qiegen Liu, Dong Liang
In order to enhance interpretability and overcome the acceleration limitations, this paper introduces an interpretable framework that unifies both $k$-space interpolation techniques and image-domain methods, grounded in the physical principles of heat diffusion equations.
no code implementations • 6 May 2023 • Taofeng Xie, Chentao Cao, Zhuoxu Cui, Yu Guo, Caiying Wu, Xuemei Wang, Qingneng Li, Zhanli Hu, Tao Sun, Ziru Sang, Yihang Zhou, Yanjie Zhu, Dong Liang, Qiyu Jin, Guoqing Chen, Haifeng Wang
JPD of MRI and noise-added PET was learned in the diffusion process.
no code implementations • 4 May 2023 • Zhuo-Xu Cui, Congcong Liu, Chentao Cao, Yuanyuan Liu, Jing Cheng, Qingyong Zhu, Yanjie Zhu, Haifeng Wang, Dong Liang
We theoretically uncovered that the combination of these challenges renders conventional deep learning methods that directly learn the mapping from a low-field MR image to a high-field MR image unsuitable.
no code implementations • 11 Apr 2023 • Zhuo-Xu Cui, Chentao Cao, Yue Wang, Sen Jia, Jing Cheng, Xin Liu, Hairong Zheng, Dong Liang, Yanjie Zhu
To overcome this challenge, we introduce a novel approach called SPIRiT-Diffusion, which is a diffusion model for k-space interpolation inspired by the iterative self-consistent SPIRiT method.
no code implementations • 14 Dec 2022 • Chentao Cao, Zhuo-Xu Cui, Jing Cheng, Sen Jia, Hairong Zheng, Dong Liang, Yanjie Zhu
Diffusion model is the most advanced method in image generation and has been successfully applied to MRI reconstruction.
no code implementations • 2 Sep 2022 • Zhuo-Xu Cui, Chentao Cao, Shaonan Liu, Qingyong Zhu, Jing Cheng, Haifeng Wang, Yanjie Zhu, Dong Liang
Recently, score-based diffusion models have shown satisfactory performance in MRI reconstruction.
1 code implementation • 10 Aug 2022 • Chentao Cao, Zhuo-Xu Cui, Yue Wang, Shaonan Liu, Taijin Chen, Hairong Zheng, Dong Liang, Yanjie Zhu
Diffusion models with continuous stochastic differential equations (SDEs) have shown superior performances in image generation.
no code implementations • 17 Jul 2022 • Yuanyuan Liu, Dong Liang, Zhuo-Xu Cui, Yuxin Yang, Chentao Cao, Qingyong Zhu, Jing Cheng, Caiyun Shi, Haifeng Wang, Yanjie Zhu
Prospective reconstruction results further demonstrate the capability of the SMART method in accelerating MR T1\r{ho} imaging.
no code implementations • 9 May 2022 • Chentao Cao, Zhuo-Xu Cui, Qingyong Zhu, Congcong Liu, Dong Liang, Yanjie Zhu
In this paper, we propose a learned low-rank method for dynamic MR imaging.