Search Results for author: Weiwen Wu

Found 14 papers, 1 papers with code

One Sample Diffusion Model in Projection Domain for Low-Dose CT Imaging

no code implementations7 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.

Computed Tomography (CT)

Generative Modeling in Sinogram Domain for Sparse-view CT Reconstruction

no code implementations25 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.

Computed Tomography (CT) Image Reconstruction

Spectral2Spectral: Image-spectral Similarity Assisted Spectral CT Deep Reconstruction without Reference

no code implementations3 Oct 2022 onghui Li, Peng He, Peng Feng, Xiaodong Guo, Weiwen Wu, Hengyong Yu

The photon-counting detector (PCD) based spectral computed tomography attracts much more attentions since it has the capability to provide more accurate identification and quantitative analysis for biomedical materials.

Multi-domain Integrative Swin Transformer network for Sparse-View Tomographic Reconstruction

no code implementations28 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.

Image Reconstruction

Stationary Multi-source AI-powered Real-time Tomography (SMART)

no code implementations27 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.

Computed Tomography (CT)

AI-Enabled Ultra-Low-Dose CT Reconstruction

no code implementations17 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.

Suppression of Correlated Noise with Similarity-based Unsupervised Deep Learning

1 code implementation6 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.

Computed Tomography (CT) Image Denoising

Deep Learning based Spectral CT Imaging

no code implementations28 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.

Computed Tomography (CT) Deblurring +2

Stabilizing Deep Tomographic Reconstruction

no code implementations4 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.

Adversarial Attack Computed Tomography (CT) +1

DLIMD: Dictionary Learning based Image-domain Material Decomposition for spectral CT

no code implementations6 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.

Computed Tomography (CT) Dictionary Learning +1

Block Matching Frame based Material Reconstruction for Spectral CT

no code implementations22 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.

Computed Tomography (CT)

Non-local Low-rank Cube-based Tensor Factorization for Spectral CT Reconstruction

no code implementations24 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.

Computed Tomography (CT)

Low-dose spectral CT reconstruction using L0 image gradient and tensor dictionary

no code implementations13 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.

Computed Tomography (CT) Dictionary Learning +1

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