Zobrazeno 1 - 10
of 946
pro vyhledávání: '"Jia, Sen"'
Autor:
Zhao, Ting, Cui, Zhuoxu, Jia, Sen, Zhu, Qingyong, Liu, Congcong, Zhou, Yihang, Zhu, Yanjie, Liang, Dong, Wang, Haifeng
Publikováno v:
ISMRM 2024 Digital poster 4024
Diffusion model has been successfully applied to MRI reconstruction, including single and multi-coil acquisition of MRI data. Simultaneous multi-slice imaging (SMS), as a method for accelerating MR acquisition, can significantly reduce scanning time,
Externí odkaz:
http://arxiv.org/abs/2408.08883
Autor:
Fu, Ying, Li, Yu, You, Shaodi, Shi, Boxin, Chen, Linwei, Zou, Yunhao, Wang, Zichun, Li, Yichen, Han, Yuze, Zhang, Yingkai, Wang, Jianan, Liu, Qinglin, Yu, Wei, Lv, Xiaoqian, Li, Jianing, Zhang, Shengping, Ji, Xiangyang, Chen, Yuanpei, Zhang, Yuhan, Peng, Weihang, Zhang, Liwen, Xu, Zhe, Gou, Dingyong, Li, Cong, Xu, Senyan, Zhang, Yunkang, Jiang, Siyuan, Lu, Xiaoqiang, Jiao, Licheng, Liu, Fang, Liu, Xu, Li, Lingling, Ma, Wenping, Yang, Shuyuan, Xie, Haiyang, Zhao, Jian, Huang, Shihua, Cheng, Peng, Shen, Xi, Wang, Zheng, An, Shuai, Zhu, Caizhi, Li, Xuelong, Zhang, Tao, Li, Liang, Liu, Yu, Yan, Chenggang, Zhang, Gengchen, Jiang, Linyan, Song, Bingyi, An, Zhuoyu, Lei, Haibo, Luo, Qing, Song, Jie, Liu, Yuan, Li, Qihang, Zhang, Haoyuan, Wang, Lingfeng, Chen, Wei, Luo, Aling, Li, Cheng, Cao, Jun, Chen, Shu, Dou, Zifei, Liu, Xinyu, Zhang, Jing, Zhang, Kexin, Yang, Yuting, Gou, Xuejian, Wang, Qinliang, Liu, Yang, Zhao, Shizhan, Zhang, Yanzhao, Yan, Libo, Guo, Yuwei, Li, Guoxin, Gao, Qiong, Che, Chenyue, Sun, Long, Chen, Xiang, Li, Hao, Pan, Jinshan, Xie, Chuanlong, Chen, Hongming, Li, Mingrui, Deng, Tianchen, Huang, Jingwei, Li, Yufeng, Wan, Fei, Xu, Bingxin, Cheng, Jian, Liu, Hongzhe, Xu, Cheng, Zou, Yuxiang, Pan, Weiguo, Dai, Songyin, Jia, Sen, Zhang, Junpei, Chen, Puhua
The intersection of physics-based vision and deep learning presents an exciting frontier for advancing computer vision technologies. By leveraging the principles of physics to inform and enhance deep learning models, we can develop more robust and ac
Externí odkaz:
http://arxiv.org/abs/2406.10744
Deep learning (DL) has emerged as a leading approach in accelerating MR imaging. It employs deep neural networks to extract knowledge from available datasets and then applies the trained networks to reconstruct accurate images from limited measuremen
Externí odkaz:
http://arxiv.org/abs/2402.02704
Publikováno v:
Chinese Physics Letters 40, 121301 (2023)
In the past twenty years, many new hadrons that are difficult to be explained within the conventional quark model have been discovered in the quarkonium region, which are called exotic hadrons. Belle II experiment, as the next-generation $B$ factory,
Externí odkaz:
http://arxiv.org/abs/2312.00403
Autor:
Cui, Zhuo-Xu, Liu, Congcong, Fan, Xiaohong, Cao, Chentao, Cheng, Jing, Zhu, Qingyong, Liu, Yuanyuan, Jia, Sen, Zhou, Yihang, Wang, Haifeng, Zhu, Yanjie, Zhang, Jianping, Liu, Qiegen, Liang, Dong
In the field of parallel imaging (PI), alongside image-domain regularization methods, substantial research has been dedicated to exploring $k$-space interpolation. However, the interpretability of these methods remains an unresolved issue. Furthermor
Externí odkaz:
http://arxiv.org/abs/2308.15918
Publikováno v:
Science Bulletin 2024; 69(10): 1386-1391
The bound state of a $\tau^+\tau^-$ pair by the electromagnetic force is the heaviest and smallest QED atom. Since the discovery of the two lightest QED atoms more than 60 years ago, no evidence for the third one has been found. We demonstrate that t
Externí odkaz:
http://arxiv.org/abs/2305.00171
Autor:
Cui, Zhuo-Xu, Cao, Chentao, Wang, Yue, Jia, Sen, Cheng, Jing, Liu, Xin, Zheng, Hairong, Liang, Dong, Zhu, Yanjie
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, existing reconstruction methods based on diffusion models are primarily f
Externí odkaz:
http://arxiv.org/abs/2304.05060
Diffusion model is the most advanced method in image generation and has been successfully applied to MRI reconstruction. However, the existing methods do not consider the characteristics of multi-coil acquisition of MRI data. Therefore, we give a new
Externí odkaz:
http://arxiv.org/abs/2212.11274
Autor:
Cui, Zhuo-Xu, Jia, Sen, Zhu, Qingyong, Liu, Congcong, Qiu, Zhilang, Liu, Yuanyuan, Cheng, Jing, Wang, Haifeng, Zhu, Yanjie, Liang, Dong
Recently, untrained neural networks (UNNs) have shown satisfactory performances for MR image reconstruction on random sampling trajectories without using additional full-sampled training data. However, the existing UNN-based approach does not fully u
Externí odkaz:
http://arxiv.org/abs/2208.05827
Lately, deep learning has been extensively investigated for accelerating dynamic magnetic resonance (MR) imaging, with encouraging progresses achieved. However, without fully sampled reference data for training, current approaches may have limited ab
Externí odkaz:
http://arxiv.org/abs/2208.03904