133 citations · 352 across the 24 of their papers we have counts for
5 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…
Low-Dose CT Using Denoising Diffusion Probabilistic Model for 20 Speedup
Wenjun Xia, Qing Lyu, Ge Wang
Low-dose computed tomography (LDCT) is an important topic in the field of radiology over the past decades. LDCT reduces ionizing radiation-induced patient health risks but it also…
SOUL-Net: A Sparse and Low-Rank Unrolling Network for Spectral CT Image Reconstruction
Xiang Chen, Wenjun Xia, Ziyuan Yang +4
Spectral computed tomography (CT) is an emerging technology, that generates a multienergy attenuation map for the interior of an object and extends the traditional image volume int…
Hypernetwork-based Personalized Federated Learning for Multi-Institutional CT Imaging
Ziyuan Yang, Wenjun Xia, Zexin Lu +3
Computed tomography (CT) is of great importance in clinical practice due to its powerful ability to provide patients' anatomical information without any invasive inspection, but it…
Physics-/Model-Based and Data-Driven Methods for Low-Dose Computed Tomography: A survey
Wenjun Xia, Hongming Shan, Ge Wang +1
Since 2016, deep learning (DL) has advanced tomographic imaging with remarkable successes, especially in low-dose computed tomography (LDCT) imaging. Despite being driven by big da…