51 Papers
84 Citations
Shi Su is an academic researcher from Chinese Academy of Sciences. The author has contributed to research in topics: Iterative reconstruction & Imaging phantom. The author has an hindex of 5, co-authored 51 publications.
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Papers
Diurnal Variations in Neural Activity of Healthy Human Brain Decoded with Resting-State Blood Oxygen Level Dependent fMRI.
TL;DR: Wide spread brain areas were found to exhibit diurnal variations, which may be attributed to the internal molecular systems regulated by clock genes, and the environmental factors including light-dark cycle, daily activities and homeostatic sleep drive.
A Dedicated 36-Channel Receive Array for Fetal MRI at 3T
Chen Qiaoyan,Guoxi Xie,Luo Chao,Xing Yang,Jin Zhu,Jo Lee,Shi Su,Dong Liang,Xiaoliang Zhang,Xin Liu,Ye Li,Hairong Zheng +11 more
TL;DR: This paper proposes a dedicated 36-channel coil array, of which size can best fit the body sizes of pregnancy gestation from 20 to 37+ weeks, and an augmented parallel imaging capability and remarkable SNR improvements at high acceleration factors.
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Parameter optimization framework on wave gradients of Wave‐CAIPI imaging
TL;DR: To propose a parameter optimization framework on wave gradients of Wave‐CAIPI imaging for decreasing g‐factor penalty and reducing reconstruction artifacts.
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Accelerated susceptibility-based positive contrast imaging of MR compatible metallic devices based on modified fast spin echo sequences.
Caiyun Shi,Guoxi Xie,Yongqin Zhang,Xiaoyong Zhang,Xiaoyong Zhang,Min Chen,Shi Su,Ying Dong,Xin Liu,Jim Ji +9 more
TL;DR: It is demonstrated that the proposed fast spin echo method can achieve good visualization of the brachytherapy seeds in positive contrast and in different orientations and is also capable of correctly differentiating brachyTherapy seeds from other similar structures on conventional magnitude images.
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Accelerating MR Imaging via Deep Chambolle-Pock Network *
Haifeng Wang,Jing Cheng,Sen Jia,Zhilang Qiu,Caiyun Shi,Zou Lixian,Shi Su,Yuchou Chang,Yanjie Zhu,Leslie Ying,Dong Liang +10 more
- 01 Jul 2019
TL;DR: A model-driven MR reconstruction is proposed that trains a deep network, named CP-net, which is derived from the Chambolle-Pock algorithm to reconstruct the in vivo MR images of human brains from highly undersampled complex k-space data acquired on different types of MR scanners.
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