D
Di Guo
Researcher at Xiamen University of Technology
Publications - 99
Citations - 2904
Di Guo is an academic researcher from Xiamen University of Technology. The author has contributed to research in topics: Compressed sensing & Iterative reconstruction. The author has an hindex of 22, co-authored 86 publications receiving 2071 citations. Previous affiliations of Di Guo include Chinese Ministry of Education & Xiamen University.
Papers
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Journal ArticleDOI
Parallel computing of patch-based nonlocal operator and its application in compressed sensing MRI.
TL;DR: A parallel architecture based on multicore processors is proposed to accelerate computations of PANO and results demonstrate that the acceleration factor approaches the number of CPU cores and overall PANO-based CS-MRI reconstruction can be accomplished in several seconds.
Posted Content
XCloud-VIP: Virtual Peak Enables Highly Accelerated NMR Spectroscopy and Faithful Quantitative Measures
Di Guo,Zhangren Tu,Yi Guo,Yirong Zhou,Jian Wang,Zi Wang,Tianyu Qiu,Min Xiao,Liubin Feng,Yuqing Huang,Donghai Lin,Yongfu You,Amir Goldbourt,Xiaobo Qu +13 more
TL;DR: In this article, a virtual peak (VIP) approach is proposed to self-learn the prior spectral information, such as the central frequency and peak lineshape, and then feed these information into the process of spectral reconstruction.
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A Faithful Deep Sensitivity Estimation for Accelerated Magnetic Resonance Imaging
Zichen Wang,Hui Fang,Chen Qian,Boxuan Shi,Lijun Bao,Liu-Hong Zhu,Jianjun Zhou,Wenping Wei,Jianzhong Lin,Di Guo,Xiaobo Qu +10 more
TL;DR: The proposed JDSI achieves the state-of-the-art performance visually and quantitatively, especially when the accelerated factor is high, and owns nice robustness to abnormal subjects and different number of autocalibration signals.
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Hypercomplex Low Rank Reconstruction for Nmr Spectroscopy with Cloud Computing ⋆
Yi-Zhen Guo,Jiaying Zhan,Zhangren Tu,Yirong Zhou,Jianfan Wu,Qing Hong,Vladislav Yu. Orekhov,Xiaobo Qu,Di Guo +8 more
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A Joint Group Sparsity-based deep learning for multi-contrast MRI reconstruction.
TL;DR: Joint Group Sparsity-based Network (JGSN) as discussed by the authors unrolls the iterative process of the joint sparsity algorithm, which includes data consistency modules, learnable sparse transform modules, and joint group sparsity constraint modules.