no code implementations • 10 Oct 2023 • Wenjun Xia, Yongyi Shi, Chuang Niu, Wenxiang Cong, Ge Wang
Computed tomography (CT) involves a patient's exposure to ionizing radiation.
no code implementations • 4 Oct 2023 • Yongyi Shi, Wenjun Xia, Ge Wang, Xuanqin Mou
Subsequently, the distorted image and dissimilarity map are combined into a multi-channel image, which is inputted into a transformer-based image quality evaluator.
no code implementations • 22 Mar 2023 • Wenjun Xia, Hsin Wu Tseng, Chuang Niu, Wenxiang Cong, Xiaohua Zhang, Shaohua Liu, Ruola Ning, Srinivasan Vedantham, Ge Wang
Specifically, in this study we transform the cutting-edge Denoising Diffusion Probabilistic Model (DDPM) into a parallel framework for sub-volume-based sparse-view breast CT image reconstruction in projection and image domains.
no code implementations • 18 Nov 2022 • Wenjun Xia, Wenxiang Cong, Ge Wang
A DDPM network based on patches extracted from fully sampled projection data is trained and then used to inpaint down-sampled projection data.
no code implementations • 29 Sep 2022 • Wenjun Xia, Qing Lyu, Ge Wang
Low-dose computed tomography (LDCT) is an important topic in the field of radiology over the past decades.
1 code implementation • 8 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.
no code implementations • 29 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.
no code implementations • 29 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.
no code implementations • 14 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.
no code implementations • 27 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.
no code implementations • 3 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.
no code implementations • 24 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.
1 code implementation • 16 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.
1 code implementation • 13 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.
no code implementations • 27 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.
no code implementations • 27 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).
no code implementations • 20 Mar 2019 • Peng Bao, Wenjun Xia, Kang Yang, Weiyan Chen, Mianyi Chen, Yan Xi, Shanzhou Niu, Jiliu Zhou, He Zhang, Huaiqiang Sun, Zhangyang Wang, Yi Zhang
Over the past few years, dictionary learning (DL)-based methods have been successfully used in various image reconstruction problems.
no code implementations • 15 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.