Search Results for author: Wenxiang Cong

Found 16 papers, 1 papers with code

Parallel Diffusion Model-based Sparse-view Cone-beam Breast CT

no code implementations22 Mar 2023 Wenjun Xia, Hsin Wu Tseng, Chuang Niu, Wenxiang Cong, Xiaohua Zhang, Shaohua Liu, Ruola Ning, Srinivasan Vedantham, Ge Wang

Specifically, in this study we transform the cutting-edge Denoising Diffusion Probabilistic Model (DDPM) into a parallel framework for sub-volume-based sparse-view breast CT image reconstruction in projection and image domains.

Computed Tomography (CT) Denoising +1

Patch-Based Denoising Diffusion Probabilistic Model for Sparse-View CT Reconstruction

no code implementations18 Nov 2022 Wenjun Xia, Wenxiang Cong, Ge Wang

A DDPM network based on patches extracted from fully sampled projection data is trained and then used to inpaint down-sampled projection data.

Computed Tomography (CT) Denoising

Phase function estimation from a diffuse optical image via deep learning

no code implementations16 Nov 2021 Yuxuan Liang, Chuang Niu, Chen Wei, Shenghan Ren, Wenxiang Cong, Ge Wang

The phase function is a key element of a light propagation model for Monte Carlo (MC) simulation, which is usually fitted with an analytic function with associated parameters.

Deep Interactive Denoiser (DID) for X-Ray Computed Tomography

no code implementations30 Nov 2020 Ti Bai, Biling Wang, Dan Nguyen, Bao Wang, Bin Dong, Wenxiang Cong, Mannudeep K. Kalra, Steve Jiang

However, there exists two challenges regarding the DL-based denoisers: 1) a trained model typically does not generate different image candidates with different noise-resolution tradeoffs which sometimes are needed for different clinical tasks; 2) the model generalizability might be an issue when the noise level in the testing images is different from that in the training dataset.

Stabilizing Deep Tomographic Reconstruction

no code implementations4 Aug 2020 Weiwen Wu, Dianlin Hu, Wenxiang Cong, Hongming Shan, Shao-Yu Wang, Chuang Niu, Pingkun Yan, Hengyong Yu, Varut Vardhanabhuti, Ge Wang

ACID synergizes a deep reconstruction network trained on big data, kernel awareness from CS-inspired processing, and iterative refinement to minimize the data residual relative to real measurement.

Adversarial Attack Computed Tomography (CT) +1

Low-dimensional Manifold Constrained Disentanglement Network for Metal Artifact Reduction

no code implementations8 Jul 2020 Chuang Niu, Wenxiang Cong, Fenglei Fan, Hongming Shan, Mengzhou Li, Jimin Liang, Ge Wang

Deep neural network based methods have achieved promising results for CT metal artifact reduction (MAR), most of which use many synthesized paired images for training.

Disentanglement Metal Artifact Reduction

Deep Efficient End-to-end Reconstruction (DEER) Network for Few-view Breast CT Image Reconstruction

1 code implementation9 Dec 2019 Huidong Xie, Hongming Shan, Wenxiang Cong, Chi Liu, Xiaohua Zhang, Shaohua Liu, Ruola Ning, Ge Wang

Breast CT provides image volumes with isotropic resolution in high contrast, enabling detection of small calcification (down to a few hundred microns in size) and subtle density differences.

Image Reconstruction

Deep-learning-based Breast CT for Radiation Dose Reduction

no code implementations25 Sep 2019 Wenxiang Cong, Hongming Shan, Xiaohua Zhang, Shaohua Liu, Ruola Ning, Ge Wang

In this study, we propose a deep-learning-based method to establish a residual neural network model for the image reconstruction, which is applied for few-view breast CT to produce high quality breast CT images.

Computed Tomography (CT) Image Reconstruction

CT Super-resolution GAN Constrained by the Identical, Residual, and Cycle Learning Ensemble(GAN-CIRCLE)

no code implementations10 Aug 2018 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

Specifically, with the generative adversarial network (GAN) as the building block, we enforce the cycle-consistency in terms of the Wasserstein distance to establish a nonlinear end-to-end mapping from noisy LR input images to denoised and deblurred HR outputs.

Computed Tomography (CT) Generative Adversarial Network +2

Structure-sensitive Multi-scale Deep Neural Network for Low-Dose CT Denoising

no code implementations2 May 2018 Chenyu You, Qingsong Yang, Hongming Shan, Lars Gjesteby, Guang Li, Shenghong Ju, Zhuiyang Zhang, Zhen Zhao, Yi Zhang, Wenxiang Cong, Ge Wang

However, the radiation dose reduction compromises the signal-to-noise ratio (SNR), leading to strong noise and artifacts that down-grade CT image quality.

Computed Tomography (CT) Denoising

3D Convolutional Encoder-Decoder Network for Low-Dose CT via Transfer Learning from a 2D Trained Network

no code implementations15 Feb 2018 Hongming Shan, Yi Zhang, Qingsong Yang, Uwe Kruger, Mannudeep K. Kalra, Ling Sun, Wenxiang Cong, Ge Wang

Based on the transfer learning from 2D to 3D, the 3D network converges faster and achieves a better denoising performance than that trained from scratch.

Computed Tomography (CT) Denoising +2

A New Type of Neurons for Machine Learning

no code implementations26 Apr 2017 Fenglei Fan, Wenxiang Cong, Ge Wang

Here we investigate the possibility of replacing the inner product with a quadratic function of the input vector, thereby upgrading the 1st order neuron to the 2nd order neuron, empowering individual neurons, and facilitating the optimization of neural networks.

BIG-bench Machine Learning Vocal Bursts Type Prediction

CT Image Reconstruction in a Low Dimensional Manifold

no code implementations16 Apr 2017 Wenxiang Cong, Ge Wang, Qingsong Yang, Jiang Hsieh, Jia Li, Rongjie Lai

In this paper, we propose a CT image reconstruction method based on the prior knowledge of the low-dimensional manifold of CT image.

Image Reconstruction

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