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Unbiased statistical image reconstruction in low-dose CT
Medical Imaging 2019: Physics of Medical Imaging
◽
10.1117/12.2512848
◽
2019
◽
Author(s):
Guang-Hong Chen
◽
Ran Zhang
◽
Chengzhu Zhang
◽
Daniel Gomez-Cardona
◽
John Hayes
Keyword(s):
Image Reconstruction
◽
Low Dose
◽
Low Dose Ct
◽
Statistical Image Reconstruction
Download Full-text
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Statistical image reconstruction for low-dose CT using nonlocal means-based regularization
Computerized Medical Imaging and Graphics
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10.1016/j.compmedimag.2014.05.002
◽
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Vol 38
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◽
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◽
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◽
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Statistical image reconstruction for low-dose CT using nonlocal means-based regularization. Part II: An adaptive approach
Computerized Medical Imaging and Graphics
◽
10.1016/j.compmedimag.2015.02.008
◽
2015
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Vol 43
◽
pp. 26-35
◽
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◽
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◽
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◽
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◽
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◽
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◽
Low Dose Ct
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Download Full-text
Iterative image reconstruction for ultra-low-dose CT with a combined low-mAs and sparse-view protocol
2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
◽
10.1109/embc.2013.6610697
◽
2013
◽
Cited By ~ 1
Author(s):
Yunwan Zhang
◽
Jing Huang
◽
Jianhua Ma
◽
Hua Zhang
◽
Zhaoying Bian
◽
...
Keyword(s):
Image Reconstruction
◽
Low Dose
◽
Iterative Image Reconstruction
◽
Low Dose Ct
Download Full-text
Optimal image reconstruction for detection and characterization of small pulmonary nodules during low-dose CT
European Radiology
◽
10.1007/s00330-014-3142-9
◽
2014
◽
Vol 24
(6)
◽
pp. 1239-1250
◽
Cited By ~ 6
Author(s):
SayedMasoud Hashemi
◽
Hatem Mehrez
◽
Richard S. C. Cobbold
◽
Narinder S. Paul
Keyword(s):
Image Reconstruction
◽
Low Dose
◽
Pulmonary Nodules
◽
Low Dose Ct
◽
Small Pulmonary Nodules
Download Full-text
Competitive performance of a modularized deep neural network compared to commercial algorithms for low-dose CT image reconstruction
Nature Machine Intelligence
◽
10.1038/s42256-019-0057-9
◽
2019
◽
Vol 1
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◽
pp. 269-276
◽
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Author(s):
Hongming Shan
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Fatemeh Homayounieh
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Uwe Kruger
◽
Ruhani Doda Khera
◽
...
Keyword(s):
Neural Network
◽
Image Reconstruction
◽
Deep Neural Network
◽
Low Dose
◽
Ct Image
◽
Competitive Performance
◽
Low Dose Ct
◽
Ct Image Reconstruction
Download Full-text
Data-Driven Adversarial Learning for Sinogram-Based Iterative Low-Dose CT Image Reconstruction
2019 23rd International Conference on System Theory, Control and Computing (ICSTCC)
◽
10.1109/icstcc.2019.8885947
◽
2019
◽
Author(s):
Anamaria Vizitiu
◽
Andrei Puiu
◽
Sureerat Reaungamornrat
◽
Lucian Mihai Itu
Keyword(s):
Image Reconstruction
◽
Low Dose
◽
Data Driven
◽
Ct Image
◽
Adversarial Learning
◽
Low Dose Ct
◽
Ct Image Reconstruction
Download Full-text
PET image reconstruction based on several respiratory-phase low-dose CT images
2012 IEEE Nuclear Science Symposium and Medical Imaging Conference Record (NSS/MIC)
◽
10.1109/nssmic.2012.6551791
◽
2012
◽
Author(s):
Woo Hyun Nam
◽
Il Jun Ahn
◽
Ji Hye Kim
◽
Kye Young Jeong
◽
Kyeong Min Kim
◽
...
Keyword(s):
Image Reconstruction
◽
Low Dose
◽
Ct Images
◽
Low Dose Ct
◽
Respiratory Phase
◽
Pet Image Reconstruction
Download Full-text
MO-DE-207A-09: Low-Dose CT Image Reconstruction Via Learning From Different Patient Normal-Dose Images
Medical Physics
◽
10.1118/1.4957237
◽
2016
◽
Vol 43
(6Part30)
◽
pp. 3702-3702
Author(s):
H Han
◽
L Xing
◽
Z Liang
Keyword(s):
Image Reconstruction
◽
Low Dose
◽
Ct Image
◽
Normal Dose
◽
Low Dose Ct
◽
Ct Image Reconstruction
Download Full-text
Image reconstruction by Mumford–Shah regularization for low-dose CT with multi-GPU acceleration
Physics in Medicine and Biology
◽
10.1088/1361-6560/ab2c85
◽
2019
◽
Vol 64
(15)
◽
pp. 155017
◽
Cited By ~ 1
Author(s):
Yining Zhu
◽
Qian Wang
◽
Mengfei Li
◽
Ming Jiang
◽
Peng Zhang
Keyword(s):
Image Reconstruction
◽
Low Dose
◽
Gpu Acceleration
◽
Low Dose Ct
Download Full-text
Deep Learning with Adaptive Hyper-parameters for Low-Dose CT Image Reconstruction
IEEE Transactions on Computational Imaging
◽
10.1109/tci.2021.3093003
◽
2021
◽
pp. 1-1
Author(s):
Qiaoqiao Ding
◽
Yuesong Nan
◽
Hao Gao
◽
Hui Ji
Keyword(s):
Deep Learning
◽
Image Reconstruction
◽
Low Dose
◽
Ct Image
◽
Low Dose Ct
◽
Ct Image Reconstruction
Download Full-text
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