G
Ge Wang
Researcher at Rensselaer Polytechnic Institute
Publications - 1006
Citations - 35703
Ge Wang is an academic researcher from Rensselaer Polytechnic Institute. The author has contributed to research in topics: Iterative reconstruction & Tomography. The author has an hindex of 75, co-authored 882 publications receiving 29839 citations. Previous affiliations of Ge Wang include Peking University & University of California.
Papers
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Book
Principles of Computerized Tomographic Imaging
TL;DR: Properties of Computerized Tomographic Imaging provides a tutorial overview of topics in tomographic imaging covering mathematical principles and theory and how to apply the theory to problems in medical imaging and other fields.
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Low-Dose CT With a Residual Encoder-Decoder Convolutional Neural Network
TL;DR: This work combines the autoencoder, deconvolution network, and shortcut connections into the residual encoder–decoder convolutional neural network (RED-CNN) for low-dose CT imaging and achieves a competitive performance relative to the-state-of-art methods in both simulated and clinical cases.
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Low-Dose CT Image Denoising Using a Generative Adversarial Network With Wasserstein Distance and Perceptual Loss
Qingsong Yang,Pingkun Yan,Yanbo Zhang,Hengyong Yu,Yongyi Shi,Xuanqin Mou,Mannudeep K. Kalra,Yi Zhang,Ling Sun,Ge Wang +9 more
TL;DR: Wang et al. as mentioned in this paper introduced a new CT image denoising method based on the generative adversarial network (GAN) with Wasserstein distance and perceptual similarity, which is capable of not only reducing the image noise level but also trying to keep the critical information at the same time.
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Low Dose CT Image Denoising Using a Generative Adversarial Network with Wasserstein Distance and Perceptual Loss
Qingsong Yang,Pingkun Yan,Yanbo Zhang,Hengyong Yu,Yongyi Shi,Xuanqin Mou,Mannudeep K. Kalra,Ge Wang +7 more
TL;DR: This paper introduces a new CT image denoising method based on the generative adversarial network (GAN) with Wasserstein distance and perceptual similarity that is capable of not only reducing the image noise level but also trying to keep the critical information at the same time.
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Low-Dose X-ray CT Reconstruction via Dictionary Learning
TL;DR: The results show that the proposed approach might produce better images with lower noise and more detailed structural features in the authors' selected cases, however, there is no proof that this is true for all kinds of structures.