11 citations · 42 across the 13 of their papers we have counts for
6 papers · 1 filter
Patch-Based Denoising Diffusion Probabilistic Model for Sparse-View CT Reconstruction
Wenjun Xia, Wenxiang Cong, Ge Wang
Sparse-view computed tomography (CT) can be used to reduce radiation dose greatly but is suffers from severe image artifacts. Recently, the deep learning based method for sparse-vi…
Deep Interactive Denoiser (DID) for X-Ray Computed Tomography
Ti Bai, Biling Wang, Dan Nguyen +5
Low dose computed tomography (LDCT) is desirable for both diagnostic imaging and image guided interventions. Denoisers are openly used to improve the quality of LDCT. Deep learning…
Low-dimensional Manifold Constrained Disentanglement Network for Metal Artifact Reduction
Chuang Niu, Wenxiang Cong, Fenglei Fan +4
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. As synthesiz…
Deep Efficient End-to-end Reconstruction (DEER) Network for Few-view Breast CT Image Reconstruction
Huidong Xie, Hongming Shan, Wenxiang Cong +5
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 di…
Dual Network Architecture for Few-view CT -- Trained on ImageNet Data and Transferred for Medical Imaging
Huidong Xie, Hongming Shan, Wenxiang Cong +4
X-ray computed tomography (CT) reconstructs cross-sectional images from projection data. However, ionizing X-ray radiation associated with CT scanning might induce cancer and genet…
CT Super-resolution GAN Constrained by the Identical, Residual, and Cycle Learning Ensemble(GAN-CIRCLE)
Chenyu You, Guang Li, Yi Zhang +9
Computed tomography (CT) is widely used in screening, diagnosis, and image-guided therapy for both clinical and research purposes. Since CT involves ionizing radiation, an overarch…