8 citations · 17 across the 7 of their papers we have counts for
6 papers · 1 filter
IDOL-Net: An Interactive Dual-Domain Parallel Network for CT Metal Artifact Reduction
Tao Wang, Wenjun Xia, Zexin Lu +5
Due to the presence of metallic implants, the imaging quality of computed tomography (CT) would be heavily degraded. With the rapid development of deep learning, several network mo…
DAN-Net: Dual-Domain Adaptive-Scaling Non-local Network for CT Metal Artifact Reduction
Tao Wang, Wenjun Xia, Yongqiang Huang +5
Metal implants can heavily attenuate X-rays in computed tomography (CT) scans, leading to severe artifacts in reconstructed images, which significantly jeopardize image quality and…
LEARN++: Recurrent Dual-Domain Reconstruction Network for Compressed Sensing CT
Yi Zhang, Hu Chen, Wenjun Xia +5
Compressed sensing (CS) computed tomography has been proven to be important for several clinical applications, such as sparse-view computed tomography (CT), digital tomosynthesis a…
Fourth-Order Nonlocal Tensor Decomposition Model for Spectral Computed Tomography
Xiang Chen, Wenjun Xia, Yan Liu +3
Spectral computed tomography (CT) can reconstruct spectral images from different energy bins using photon counting detectors (PCDs). However, due to the limited photons and countin…
CT Reconstruction with PDF: Parameter-Dependent Framework for Multiple Scanning Geometries and Dose Levels
Wenjun Xia, Zexin Lu, Yongqiang Huang +4
Current mainstream of CT reconstruction methods based on deep learning usually needs to fix the scanning geometry and dose level, which will significantly aggravate the training co…
Visual Attention Network for Low Dose CT
Wenchao Du, Hu Chen, Peixi Liao +3
Noise and artifacts are intrinsic to low dose CT (LDCT) data acquisition, and will significantly affect the imaging performance. Perfect noise removal and image restoration is intr…