3 citations · 8 across the 4 of their papers we have counts for
7 papers
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…
TED-net: Convolution-free T2T Vision Transformer-based Encoder-decoder Dilation network for Low-dose CT Denoising
Dayang Wang, Zhan Wu, Hengyong Yu
Low dose computed tomography is a mainstream for clinical applications. How-ever, compared to normal dose CT, in the low dose CT (LDCT) images, there are stronger noise and more ar…
EZLDA: Efficient and Scalable LDA on GPUs
Shilong Wang, Hang Liu, Anil Gaihre +1
LDA is a statistical approach for topic modeling with a wide range of applications. However, there exist very few attempts to accelerate LDA on GPUs which come with exceptional com…
MetaInv-Net: Meta Inversion Network for Sparse View CT Image Reconstruction
Haimiao Zhang, Baodong Liu, Hengyong Yu +1
X-ray Computed Tomography (CT) is widely used in clinical applications such as diagnosis and image-guided interventions. In this paper, we propose a new deep learning based model f…
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…
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…