77 citations · 183 across the 18 of their papers we have counts for
19 papers
Stage-by-stage Wavelet Optimization Refinement Diffusion Model for Sparse-View CT Reconstruction
Kai Xu, Shiyu Lu, Bin Huang +2
Diffusion models have emerged as potential tools to tackle the challenge of sparse-view CT reconstruction, displaying superior performance compared to conventional methods. Neverth…
Data-iterative Optimization Score Model for Stable Ultra-Sparse-View CT Reconstruction
Weiwen Wu, Yanyang Wang
Score-based generative models (SGMs) have gained prominence in sparse-view CT reconstruction for their precise sampling of complex distributions. In SGM-based reconstruction, data…
Two-and-a-half Order Score-based Model for Solving 3D Ill-posed Inverse Problems
Zirong Li, Yanyang Wang, Jianjia Zhang +2
Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) are crucial technologies in the field of medical imaging. Score-based models have proven to be effective in addressing…
One Sample Diffusion Model in Projection Domain for Low-Dose CT Imaging
Bin Huang, Liu Zhang, Shiyu Lu +3
Low-dose computed tomography (CT) plays a significant role in reducing the radiation risk in clinical applications. However, lowering the radiation dose will significantly degrade…
Generative Modeling in Sinogram Domain for Sparse-view CT Reconstruction
Bing Guan, Cailian Yang, Liu Zhang +5
The radiation dose in computed tomography (CT) examinations is harmful for patients but can be significantly reduced by intuitively decreasing the number of projection views. Reduc…
Multi-domain Integrative Swin Transformer network for Sparse-View Tomographic Reconstruction
Jiayi Pan, Heye Zhang, Weifei Wu +2
Decreasing projection views to lower X-ray radiation dose usually leads to severe streak artifacts. To improve image quality from sparse-view data, a Multi-domain Integrative Swin…
Stationary Multi-source AI-powered Real-time Tomography (SMART)
Weiwen Wu, Yaohui Tang, Tianling Lv +6
Over the past decades, the development of CT technologies has been largely driven by the needs for cardiac imaging but the temporal resolution remains insufficient for clinical CT…
AI-Enabled Ultra-Low-Dose CT Reconstruction
Weiwen Wu, Chuang Niu, Shadi Ebrahimian +3
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. Wi…
Deep Iteration Assisted by Multi-level Obey-pixel Network Discriminator (DIAMOND) for Medical Image Recovery
Moran Xu, Dianlin Hu, Weifei Wu +1
Image restoration is a typical ill-posed problem, and it contains various tasks. In the medical imaging field, an ill-posed image interrupts diagnosis and even following image proc…
Clinical Micro-CT Empowered by Interior Tomography, Robotic Scanning, and Deep Learning
Mengzhou Li, Zheng Fang, Wenxiang Cong +6
While micro-CT systems are instrumental in preclinical research, clinical micro-CT imaging has long been desired with cochlear implantation as a primary example. The structural det…
Suppression of Correlated Noise with Similarity-based Unsupervised Deep Learning
Chuang Niu, Mengzhou Li, Fenglei Fan +4
Image denoising is a prerequisite for downstream tasks in many fields. Low-dose and photon-counting computed tomography (CT) denoising can optimize diagnostic performance at minimi…
Deep Learning based Spectral CT Imaging
Weiwen Wu, Dianlin Hu, Chuang Niu +7
Spectral computed tomography (CT) has attracted much attention in radiation dose reduction, metal artifacts removal, tissue quantification and material discrimination. The x-ray en…
Stabilizing Deep Tomographic Reconstruction
Weiwen Wu, Dianlin Hu, Wenxiang Cong +7
Tomographic image reconstruction with deep learning is an emerging field, but a recent landmark study reveals that several deep reconstruction networks are unstable for computed to…
Improved Material Decomposition with a Two-step Regularization for spectral CT
Weiwen Wu, Peijun Chen, Vince Vardhanabhuti +2
One of the advantages of spectral computed tomography (CT) is it can achieve accurate material components using the material decomposition methods. The image-based material decompo…
DLIMD: Dictionary Learning based Image-domain Material Decomposition for spectral CT
Weiwen Wu, Haijun Yu, Peijun Chen +7
The potential huge advantage of spectral computed tomography (CT) is its capability to provide accuracy material identification and quantitative tissue information. This can benefi…
Block Matching Frame based Material Reconstruction for Spectral CT
Weiwen Wu, Qian Wang, Fenglin Liu +2
Spectral computed tomography (CT) has a great potential in material identification and decomposition. To achieve high-quality material composition images and further suppress the x…
Non-local Low-rank Cube-based Tensor Factorization for Spectral CT Reconstruction
Weiwen Wu, Fenglin Liu, Yanbo Zhang +2
Spectral computed tomography (CT) reconstructs material-dependent attenuation images with the projections of multiple narrow energy windows, it is meaningful for material identific…
Low-dose spectral CT reconstruction using L0 image gradient and tensor dictionary
Weiwen Wu, Yanbo Zhang, Qian Wang +3
Spectral computed tomography (CT) has a great superiority in lesion detection, tissue characterization and material decomposition. To further extend its potential clinical applicat…
BPF-type Region-of-interest Reconstruction for Parallel Translational Computed Tomography
Weiwen Wu, Hengyong Yu, Shaoyu Wang +1
Recently, an ultra-low-cost linear scan based tomography architecture was proposed by our team. Similar to linear tomosynthesis, the source and detector are translated in opposite…