77 citations · 183 across the 18 of their papers we have counts for
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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…
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…
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…
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…