Search Results for author: Wenjun Xia

Found 13 papers, 2 papers with code

Hypernetwork-based Personalized Federated Learning for Multi-Institutional CT Imaging

no code implementations8 Jun 2022 Ziyuan Yang, Wenjun Xia, Zexin Lu, Yingyu Chen, Xiaoxiao Li, Yi Zhang

The basic assumption of HyperFed is that the optimization problem for each institution can be divided into two parts: the local data adaption problem and the global CT imaging problem, which are implemented by an institution-specific hypernetwork and a global-sharing imaging network, respectively.

Computed Tomography (CT) Personalized Federated Learning

Synergizing Physics/Model-based and Data-driven Methods for Low-Dose CT

no code implementations29 Mar 2022 Wenjun Xia, Hongming Shan, Ge Wang, Yi Zhang

Since 2016, deep learning (DL) has advanced tomographic imaging with remarkable successes, especially in low-dose computed tomography (LDCT) imaging.


Unsupervised PET Reconstruction from a Bayesian Perspective

no code implementations29 Oct 2021 Chenyu Shen, Wenjun Xia, Hongwei Ye, Mingzheng Hou, Hu Chen, Yan Liu, Jiliu Zhou, Yi Zhang

Positron emission tomography (PET) reconstruction has become an ill-posed inverse problem due to low-count projection data, and a robust algorithm is urgently required to improve imaging quality.

Denoising Image Restoration

One Network to Solve Them All: A Sequential Multi-Task Joint Learning Network Framework for MR Imaging Pipeline

no code implementations14 May 2021 Zhiwen Wang, Wenjun Xia, Zexin Lu, Yongqiang Huang, Yan Liu, Hu Chen, Jiliu Zhou, Yi Zhang

Magnetic resonance imaging (MRI) acquisition, reconstruction, and segmentation are usually processed independently in the conventional practice of MRI workflow.

Provably Convergent Learned Inexact Descent Algorithm for Low-Dose CT Reconstruction

no code implementations27 Apr 2021 Qingchao Zhang, Mehrdad Alvandipour, Wenjun Xia, Yi Zhang, Xiaojing Ye, YunMei Chen

We propose a provably convergent method, called Efficient Learned Descent Algorithm (ELDA), for low-dose CT (LDCT) reconstruction.

IDOL-Net: An Interactive Dual-Domain Parallel Network for CT Metal Artifact Reduction

no code implementations3 Apr 2021 Tao Wang, Wenjun Xia, Zexin Lu, Huaiqiang Sun, Yan Liu, Hu Chen, Jiliu Zhou, Yi Zhang

Since the dual-domain MAR methods can leverage the hybrid information from both sinogram and image domains, they have significantly improved the performance compared to single-domain methods.

Computed Tomography (CT) Disentanglement +1

MANAS: Multi-Scale and Multi-Level Neural Architecture Search for Low-Dose CT Denoising

no code implementations24 Mar 2021 Zexin Lu, Wenjun Xia, Yongqiang Huang, Hongming Shan, Hu Chen, Jiliu Zhou, Yi Zhang

Recent advance on neural network architecture search (NAS) has proved that the network architecture has a dramatic effect on the model performance, which indicates that current network architectures for LDCT may be sub-optimal.

Computed Tomography (CT) Denoising +1

DAN-Net: Dual-Domain Adaptive-Scaling Non-local Network for CT Metal Artifact Reduction

1 code implementation16 Feb 2021 Tao Wang, Wenjun Xia, Yongqiang Huang, Huaiqiang Sun, Yan Liu, Hu Chen, Jiliu Zhou, Yi Zhang

With the rapid development of deep learning in the field of medical imaging, several network models have been proposed for metal artifact reduction (MAR) in CT.

Computed Tomography (CT) Metal Artifact Reduction

LEARN++: Recurrent Dual-Domain Reconstruction Network for Compressed Sensing CT

1 code implementation13 Dec 2020 Yi Zhang, Hu Chen, Wenjun Xia, Yang Chen, Baodong Liu, Yan Liu, Huaiqiang Sun, Jiliu Zhou

Compressed sensing (CS) computed tomography has been proven to be important for several clinical applications, such as sparse-view computed tomography (CT), digital tomosynthesis and interior tomography.

Computed Tomography (CT) Image Restoration

CT Reconstruction with PDF: Parameter-Dependent Framework for Multiple Scanning Geometries and Dose Levels

no code implementations27 Oct 2020 Wenjun Xia, Zexin Lu, Yongqiang Huang, Yan Liu, Hu Chen, Jiliu Zhou, Yi Zhang

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 cost and need more training data for clinical application.

Fourth-Order Nonlocal Tensor Decomposition Model for Spectral Computed Tomography

no code implementations27 Oct 2020 Xiang Chen, Wenjun Xia, Yan Liu, Hu Chen, Jiliu Zhou, Yi Zhang

Spectral computed tomography (CT) can reconstruct spectral images from different energy bins using photon counting detectors (PCDs).

Computed Tomography (CT) Image Reconstruction +1

Sparse-View CT Reconstruction via Convolutional Sparse Coding

no code implementations15 Oct 2018 Peng Bao, Wenjun Xia, Kang Yang, Jiliu Zhou, Yi Zhang

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.

Dictionary Learning

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