6 citations · 11 across the 5 of their papers we have counts for
8 papers
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…
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…
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…