Artifact Removal using Improved GoogLeNet for Sparse-view CT Reconstruction
Abstract Sparse-view Reconstruction can be used to provide accelerated low dose CT imaging with both accelerated scan and reduced projection/back-projection calculation. Despite the rapid developments, image noise and artifacts still remain a major issue in the low dose protocol. In this paper, a de...
Enregistré dans:
Auteurs principaux: | Shipeng Xie, Xinyu Zheng, Yang Chen, Lizhe Xie, Jin Liu, Yudong Zhang, Jingjie Yan, Hu Zhu, Yining Hu |
---|---|
Format: | article |
Langue: | EN |
Publié: |
Nature Portfolio
2018
|
Sujets: | |
Accès en ligne: | https://doaj.org/article/b8cc37968daf44898d6e86aee02c35ce |
Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
Documents similaires
-
Power Efficient Design of High-Performance Convolutional Neural Networks Hardware Accelerator on FPGA: A Case Study With GoogLeNet
par: Ahmed J. Abd El-Maksoud, et autres
Publié: (2021) -
Sparse-View Neutron CT Reconstruction Using a Modified Weighted Total Difference Minimization Method
par: Yapeng Wu, et autres
Publié: (2021) -
Iterative image reconstruction for sparse-view CT using normal-dose image induced total variation prior.
par: Jing Huang, et autres
Publié: (2013) -
Discriminative Prior - Prior Image Constrained Compressed Sensing Reconstruction for Low-Dose CT Imaging
par: Yang Chen, et autres
Publié: (2017) -
Plug-and-Play ADMM for MRI Reconstruction With Convex Nonconvex Sparse Regularization
par: Jincheng Li, et autres
Publié: (2021)