Zobrazeno 1 - 5
of 5
pro vyhledávání: '"Xin, Bingyu"'
Autor:
Lyu, Jun, Qin, Chen, Wang, Shuo, Wang, Fanwen, Li, Yan, Wang, Zi, Guo, Kunyuan, Ouyang, Cheng, Tänzer, Michael, Liu, Meng, Sun, Longyu, Sun, Mengting, Li, Qin, Shi, Zhang, Hua, Sha, Li, Hao, Chen, Zhensen, Zhang, Zhenlin, Xin, Bingyu, Metaxas, Dimitris N., Yiasemis, George, Teuwen, Jonas, Zhang, Liping, Chen, Weitian, Zhao, Yidong, Tao, Qian, Pang, Yanwei, Liu, Xiaohan, Razumov, Artem, Dylov, Dmitry V., Dou, Quan, Yan, Kang, Xue, Yuyang, Du, Yuning, Dietlmeier, Julia, Garcia-Cabrera, Carles, Hemidi, Ziad Al-Haj, Vogt, Nora, Xu, Ziqiang, Zhang, Yajing, Chu, Ying-Hua, Chen, Weibo, Bai, Wenjia, Zhuang, Xiahai, Qin, Jing, Wu, Lianmin, Yang, Guang, Qu, Xiaobo, Wang, He, Wang, Chengyan
Cardiac MRI, crucial for evaluating heart structure and function, faces limitations like slow imaging and motion artifacts. Undersampling reconstruction, especially data-driven algorithms, has emerged as a promising solution to accelerate scans and e
Externí odkaz:
http://arxiv.org/abs/2404.01082
The key to dynamic or multi-contrast magnetic resonance imaging (MRI) reconstruction lies in exploring inter-frame or inter-contrast information. Currently, the unrolled model, an approach combining iterative MRI reconstruction steps with learnable n
Externí odkaz:
http://arxiv.org/abs/2309.13839
Autor:
Xin, Bingyu1 (AUTHOR) xinby07@163.com, Huang, Zhiyong2,3 (AUTHOR) zhiyongh041@gmail.com, Huang, Shijie4 (AUTHOR) 107552101298@stu.xju.edu.cn, Feng, Liang1,5 (AUTHOR) liang.feng@jxust.edu.cn
Publikováno v:
Sensors (14248220). Aug2024, Vol. 24 Issue 15, p4892. 20p.
Magnetic Resonance (MR) image reconstruction from highly undersampled $k$-space data is critical in accelerated MR imaging (MRI) techniques. In recent years, deep learning-based methods have shown great potential in this task. This paper proposes a l
Externí odkaz:
http://arxiv.org/abs/2112.09760
Magnetic Resonance (MR) images of different modalities can provide complementary information for clinical diagnosis, but whole modalities are often costly to access. Most existing methods only focus on synthesizing missing images between two modaliti
Externí odkaz:
http://arxiv.org/abs/2005.00925