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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…
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…
MANAS: Multi-Scale and Multi-Level Neural Architecture Search for Low-Dose CT Denoising
Zexin Lu, Wenjun Xia, Yongqiang Huang +4
Lowering the radiation dose in computed tomography (CT) can greatly reduce the potential risk to public health. However, the reconstructed images from the dose-reduced CT or low-do…
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…
MD-Recon-Net: A Parallel Dual-Domain Convolutional Neural Network for Compressed Sensing MRI
Maosong Ran, Wenjun Xia, Yongqiang Huang +6
Compressed sensing magnetic resonance imaging (CS-MRI) is a theoretical framework that can accurately reconstruct images from undersampled k-space data with a much lower sampling r…
Convolutional Sparse Coding for Compressed Sensing CT Reconstruction
Peng Bao, Wenjun Xia, Kang Yang +9
Over the past few years, dictionary learning (DL)-based methods have been successfully used in various image reconstruction problems. However, traditional DL-based computed tomogra…
Sparse-View CT Reconstruction via Convolutional Sparse Coding
Peng Bao, Wenjun Xia, Kang Yang +2
Traditional dictionary learning based CT reconstruction methods are patch-based and the features learned with these methods often contain shifted versions of the same features. To…
Few-View CT Reconstruction with Group-Sparsity Regularization
Peng Bao, Jiliu Zhou, Yi Zhang
Classical total variation (TV) based iterative reconstruction algorithms assume that the signal is piecewise smooth, which causes reconstruction results to suffer from the over-smo…
LEARN: Learned Experts' Assessment-based Reconstruction Network for Sparse-data CT
Hu Chen, Yi Zhang, Yunjin Chen +7
Compressive sensing (CS) has proved effective for tomographic reconstruction from sparsely collected data or under-sampled measurements, which are practically important for few-vie…
Low-Dose CT with a Residual Encoder-Decoder Convolutional Neural Network (RED-CNN)
Hu Chen, Yi Zhang, Mannudeep K. Kalra +5
Given the potential X-ray radiation risk to the patient, low-dose CT has attracted a considerable interest in the medical imaging field. The current main stream low-dose CT methods…
Low-dose CT denoising with convolutional neural network
Hu Chen, Yi Zhang, Weihua Zhang +4
To reduce the potential radiation risk, low-dose CT has attracted much attention. However, simply lowering the radiation dose will lead to significant deterioration of the image qu…
Low-Dose CT via Deep Neural Network
Hu Chen, Yi Zhang, Weihua Zhang +4
In order to reduce the potential radiation risk, low-dose CT has attracted more and more attention. However, simply lowering the radiation dose will significantly degrade the imagi…