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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Magnetic resonance image reconstruction from undersampled measurements using a patch-based nonlocal operator
TL;DR: This paper designs a patch-based nonlocal operator (PANO) to sparsify magnetic resonance images by making use of the similarity of image patches to achieve lower reconstruction error and higher visual quality than conventional CS-MRI methods.
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Convolutional Neural Networks-Based MRI Image Analysis for the Alzheimer’s Disease Prediction From Mild Cognitive Impairment
Weiming Lin,Tong Tong,Qinquan Gao,Di Guo,Xiaofeng Du,Yonggui Yang,Gang Guo,Min Xiao,Min Du,Xiaobo Qu +9 more
TL;DR: A deep learning approach based on convolutional neural networks, designed to accurately predict MCI-to-AD conversion with magnetic resonance imaging (MRI) data, outperforms others with higher accuracy and AUC, while keeping a good balance between the sensitivity and specificity.
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Undersampled MRI reconstruction with patch-based directional wavelets
TL;DR: Simulation results on phantom and in vivo data indicate that the proposed patch-based directional wavelets method outperforms conventional compressed sensing MRI methods in preserving the edges and suppressing the noise.
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Fast Multiclass Dictionaries Learning With Geometrical Directions in MRI Reconstruction
TL;DR: The proposed method can be exploited in undersampled magnetic resonance imaging to reduce data acquisition time and reconstruct images with better image quality and the computation of the proposed approach is much faster than the typical K-SVD dictionary learning method in magnetic resonance image reconstruction.
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Iterative thresholding compressed sensing MRI based on contourlet transform
TL;DR: Simulation results demonstrate that contourlet-based CS-MRI can better reconstruct the curves and edges than traditional wavelet- based methods, especially at low k-space sampling rate.