W
Wenxiang Cong
Researcher at Rensselaer Polytechnic Institute
Publications - 211
Citations - 4762
Wenxiang Cong 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 32, co-authored 200 publications receiving 3952 citations. Previous affiliations of Wenxiang Cong include Virginia Tech & University of Iowa.
Papers
More filters
Journal ArticleDOI
3-D Convolutional Encoder-Decoder Network for Low-Dose CT via Transfer Learning From a 2-D Trained Network
Hongming Shan,Yi Zhang,Qingsong Yang,Uwe Kruger,Mannudeep K. Kalra,Ling Sun,Wenxiang Cong,Ge Wang +7 more
TL;DR: A conveying path-based convolutional encoder-decoder (CPCE) network in 2-D and 3-D configurations within the GAN framework for LDCT denoising, which has a better performance in that it suppresses image noise and preserves subtle structures.
Journal ArticleDOI
Practical reconstruction method for bioluminescence tomography
Wenxiang Cong,Ge Wang,D. Kumar,Yi Liu,Ming Jiang,Lihong V. Wang,Eric A. Hoffman,Geoffrey McLennan,Paul B. McCray,Joseph Zabner,Alexander X. Cong +10 more
TL;DR: A direct linear relationship between measured surface photon density and an unknown bioluminescence source distribution is established by using a finite-element method based on the diffusion approximation to the photon propagation in biological tissue.
Journal ArticleDOI
CT Super-Resolution GAN Constrained by the Identical, Residual, and Cycle Learning Ensemble (GAN-CIRCLE)
Chenyu You,Wenxiang Cong,Michael W. Vannier,Punam K. Saha,Eric A. Hoffman,Ge Wang,Guang Li,Yi Zhang,Xiaoliu Zhang,Hongming Shan,Mengzhou Li,Shenghong Ju,Zhen Zhao,Zhuiyang Zhang +13 more
TL;DR: Wang et al. as mentioned in this paper proposed a semi-supervised deep learning approach to recover high-resolution (HR) CT images from low resolution (LR) counterparts by enforcing the cycle-consistency in terms of the Wasserstein distance.
Journal ArticleDOI
CT Super-resolution GAN Constrained by the Identical, Residual, and Cycle Learning Ensemble(GAN-CIRCLE)
Chenyu You,Guang Li,Yi Zhang,Xiaoliu Zhang,Hongming Shan,Shenghong Ju,Zhen Zhao,Zhuiyang Zhang,Wenxiang Cong,Michael W. Vannier,Punam K. Saha,Ge Wang +11 more
TL;DR: In this article, a semi-supervised deep learning approach was proposed to recover high-resolution (HR) CT images from low resolution (LR) counterparts by enforcing the cycle-consistency in terms of Wasserstein distance to establish a nonlinear end-to-end mapping from noisy LR input images to denoised and deblurred HR outputs.
Journal ArticleDOI
Structurally-Sensitive Multi-Scale Deep Neural Network for Low-Dose CT Denoising
Chenyu You,Qingsong Yang,Hongming Shan,Lars Gjesteby,Guang Li,Shenghong Ju,Zhuiyang Zhang,Zhen Zhao,Yi Zhang,Wenxiang Cong,Ge Wang +10 more
TL;DR: This paper proposes a novel 3-D noise reduction method, called structurally sensitive multi-scale generative adversarial net, to improve the low-dose CT image quality, which incorporates3-D volumetric information to improved the image quality.