no code implementations • 29 Jan 2024 • Jiahao Huang, Yinzhe Wu, Fanwen Wang, Yingying Fang, Yang Nan, Cagan Alkan, Lei Xu, Zhifan Gao, Weiwen Wu, Lei Zhu, Zhaolin Chen, Peter Lally, Neal Bangerter, Kawin Setsompop, Yike Guo, Daniel Rueckert, Ge Wang, Guang Yang
Magnetic Resonance Imaging (MRI) is a pivotal clinical diagnostic tool, yet its extended scanning times often compromise patient comfort and image quality, especially in volumetric, temporal and quantitative scans.
1 code implementation • 30 Aug 2023 • Kai Xu, Shiyu Lu, Bin Huang, Weiwen Wu, Qiegen Liu
Diffusion models have emerged as potential tools to tackle the challenge of sparse-view CT reconstruction, displaying superior performance compared to conventional methods.
no code implementations • 28 Aug 2023 • Weiwen Wu, Yanyang Wang
Additionally, we pioneer an inference strategy that traces back from current iteration results to ideal truth, enhancing reconstruction stability.
1 code implementation • 16 Aug 2023 • Zirong Li, Yanyang Wang, Jianjia Zhang, Weiwen Wu, Hengyong Yu
Score-based models have proven to be effective in addressing different inverse problems encountered in CT and MRI, such as sparse-view CT and fast MRI reconstruction.
2 code implementations • 7 Dec 2022 • Bin Huang, Liu Zhang, Shiyu Lu, Boyu Lin, Weiwen Wu, Qiegen Liu
Therefore, we propose a fully unsupervised one sample diffusion model (OSDM)in projection domain for low-dose CT reconstruction.
1 code implementation • 25 Nov 2022 • Bing Guan, Cailian Yang, Liu Zhang, Shanzhou Niu, Minghui Zhang, Yuhao Wang, Weiwen Wu, Qiegen Liu
When the number of projection view changes, the DL network should be retrained with updated sparse-view/full-view CT image pairs.
no code implementations • 3 Oct 2022 • Xiaodong Guo, Longhui Li, Dingyue Chang, Peng He, Peng Feng, Hengyong Yu, Weiwen Wu
Spectral computed tomography based on a photon-counting detector (PCD) attracts more and more attentions since it has the capability to provide more accurate identification and quantitative analysis for biomedical materials.
no code implementations • 28 Nov 2021 • Jiayi Pan, Heye Zhang, Weifei Wu, Zhifan Gao, Weiwen Wu
To improve image quality from sparse-view data, a Multi-domain Integrative Swin Transformer network (MIST-net) was developed in this article.
no code implementations • 27 Aug 2021 • Weiwen Wu, Yaohui Tang, Tianling Lv, Chuang Niu, Cheng Wang, Yiyan Guo, Yunheng Chang, Ge Wang, Yan Xi
The reconstructed volumetric images convincingly demonstrate the merits of the SMART system using the AI-empowered interior tomography approach, enabling cardiac micro-CT with the unprecedented temporal resolution of 30ms, which is an order of magnitude higher than the state of the art.
no code implementations • 17 Jun 2021 • Weiwen Wu, Chuang Niu, Shadi Ebrahimian, Hengyong Yu, Mannu Kalra, Ge Wang
By the ALARA (As Low As Reasonably Achievable) principle, ultra-low-dose CT reconstruction is a holy grail to minimize cancer risks and genetic damages, especially for children.
no code implementations • 8 Feb 2021 • Moran Xu, Dianlin Hu, Weifei Wu, Weiwen Wu
Image restoration is a typical ill-posed problem, and it contains various tasks.
1 code implementation • 6 Nov 2020 • Chuang Niu, Mengzhou Li, Fenglei Fan, Weiwen Wu, Xiaodong Guo, Qing Lyu, Ge Wang
Limited by the independent noise assumption, current unsupervised denoising methods cannot process correlated noises as in CT images.
no code implementations • 28 Aug 2020 • Weiwen Wu, Dianlin Hu, Chuang Niu, Lieza Vanden Broeke, Anthony P. H. Butler, Peng Cao, James Atlas, Alexander Chernoglazov, Varut Vardhanabhuti, Ge Wang
To address the image deblurring problem associated with the $L_2^2$-loss, we propose a general $L_p^p$-loss, $p>0$ Furthermore, the images from different energy bins share similar structures of the same object, the regularization characterizing correlations of different energy bins is incorporated into the $L_p^p$-loss function, which helps unify the deep learning based methods with traditional compressed sensing based methods.
no code implementations • 4 Aug 2020 • Weiwen Wu, Dianlin Hu, Wenxiang Cong, Hongming Shan, Shao-Yu Wang, Chuang Niu, Pingkun Yan, Hengyong Yu, Varut Vardhanabhuti, Ge Wang
ACID synergizes a deep reconstruction network trained on big data, kernel awareness from CS-inspired processing, and iterative refinement to minimize the data residual relative to real measurement.
no code implementations • 6 May 2019 • Weiwen Wu, Haijun Yu, Peijun Chen, Fulin Luo, Fenglin Liu, Qian Wang, Yining Zhu, Yanbo Zhang, Jian Feng, Hengyong Yu
Second, we employ the direct inversion (DI) method to obtain initial material decomposition results, and a set of image patches are extracted from the mode-1 unfolding of normalized material image tensor to train a united dictionary by the K-SVD technique.
no code implementations • 22 Oct 2018 • Weiwen Wu, Qian Wang, Fenglin Liu, Yining Zhu, Hengyong Yu
Spectral computed tomography (CT) has a great potential in material identification and decomposition.
no code implementations • 24 Jul 2018 • Weiwen Wu, Fenglin Liu, Yanbo Zhang, Qian Wang, Hengyong Yu
Then, as a new regularizer, Kronecker-Basis-Representation (KBR) tensor factorization is employed into a basic spectral CT reconstruction model to enhance the ability of extracting image features and protecting spatial edges, generating the non-local low-rank cube-based tensor factorization (NLCTF) method.
no code implementations • 13 Dec 2017 • Weiwen Wu, Yanbo Zhang, Qian Wang, Fenglin Liu, Peijun Chen, Hengyong Yu
The L0TDL method inherits the advantages of tensor dictionary learning (TDL) by employing the similarity of spectral CT images.