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Image reconstruction; Computerized tomography; Peeling;
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Image reconstruction; Radon transform; Projection moments;
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1001
Discrete Radon transform; Discrete orthogonal moments; Projection moments; Image reconstruction
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1103
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1209
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1701
Computed tomography
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computerised tomography
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Image reconstruction techniques; X-ray imaging; Tomographic imaging
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1412
BibRef
Earlier:
Binary tomography reconstructions of bone microstructure from few
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ICIP14(1778-1782)
IEEE DOI
1502
Bones
BibRef
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Denis, L.,
Burnier, C.,
Thiebaut, E.,
Becker, J.M.,
Desbat, L.,
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IP(24), No. 12, December 2015, pp. 4715-4725.
IEEE DOI
1512
computational complexity
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Zhang, S.T.[Shao-Ting],
Chen, T.H.[Tsu-Han],
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Regularization,
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IEEE DOI
1507
Computed tomography
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Ni, M.[Ming],
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Deconvolution,
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1608
BibRef
Zhang, H.,
Han, H.,
Liang, Z.,
Hu, Y.,
Liu, Y.,
Moore, W.,
Ma, J.,
Lu, H.,
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Knowledge-Based Bayesian Reconstruction of Current Low-Dose CT Images,
MedImg(35), No. 3, March 2016, pp. 860-870.
IEEE DOI
1603
BibRef
And:
Erratum:
MedImg(35), No. 6, June 2016, pp. 1587-1587.
IEEE DOI
1606
Bayes methods
BibRef
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Palenstijn, W.J.,
Batenburg, K.J.,
TVR-DART: A More Robust Algorithm for Discrete Tomography From
Limited Projection Data With Automated Gray Value Estimation,
IP(25), No. 1, January 2016, pp. 455-468.
IEEE DOI
1601
Computed tomography
BibRef
Lukic, T.[Tibor],
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Binary tomography reconstruction based on shape orientation,
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Elsevier DOI
1608
Discrete tomography
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Lukic, T.[Tibor],
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IWCIA20(219-235).
Springer DOI
2009
BibRef
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Lukic, T.[Tibor],
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orientation,
IET-IPR(14), No. 1, January 2020, pp. 25-30.
DOI Link
1912
BibRef
Wang, G.,
Zhou, J.,
Yu, Z.,
Wang, W.,
Qi, J.,
Hybrid Pre-Log and Post-Log Image Reconstruction for Computed
Tomography,
MedImg(36), No. 12, December 2017, pp. 2457-2465.
IEEE DOI
1712
Computational modeling, Computed tomography, Convergence,
Data models, Image reconstruction, Noise measurement, Low-dose CT,
weighted least squares
BibRef
Wu, D.,
Kim, K.,
El Fakhri, G.,
Li, Q.,
Iterative Low-Dose CT Reconstruction With Priors Trained by
Artificial Neural Network,
MedImg(36), No. 12, December 2017, pp. 2479-2486.
IEEE DOI
1712
Computed tomography, Decoding, Image reconstruction, Manifolds,
Neural networks, Optimization, Reconstruction algorithms,
reconstruction algorithms
BibRef
Xie, Q.,
Zeng, D.,
Zhao, Q.,
Meng, D.,
Xu, Z.,
Liang, Z.,
Ma, J.,
Robust Low-Dose CT Sinogram Preprocessing via Exploiting
Noise-Generating Mechanism,
MedImg(36), No. 12, December 2017, pp. 2487-2498.
IEEE DOI
1712
Biomedical imaging, Computed tomography, Data models,
Image reconstruction, Noise measurement, Photonics, X-ray imaging,
statistical model
BibRef
Liu, J.,
Ma, J.,
Zhang, Y.,
Chen, Y.,
Yang, J.,
Shu, H.,
Luo, L.,
Coatrieux, G.,
Yang, W.,
Feng, Q.,
Chen, W.,
Discriminative Feature Representation to Improve Projection Data
Inconsistency for Low Dose CT Imaging,
MedImg(36), No. 12, December 2017, pp. 2499-2509.
IEEE DOI
1712
Attenuation, Computed tomography, Dictionaries,
Image reconstruction, Image restoration, Noise measurement,
tissue attenuation features
BibRef
Liu, J.,
Hu, Y.,
Yang, J.,
Chen, Y.,
Shu, H.,
Luo, L.,
Feng, Q.,
Gui, Z.,
Coatrieux, G.,
3D Feature Constrained Reconstruction for Low-Dose CT Imaging,
CirSysVideo(28), No. 5, May 2018, pp. 1232-1247.
IEEE DOI
1805
Algorithm design and analysis, Computed tomography, Dictionaries,
Image quality, Image reconstruction, Minimization,
low-dose computed tomography (LDCT)
BibRef
Chen, H.,
Zhang, Y.,
Kalra, M.K.,
Lin, F.,
Chen, Y.,
Liao, P.,
Zhou, J.,
Wang, G.,
Low-Dose CT With a Residual Encoder-Decoder Convolutional Neural
Network,
MedImg(36), No. 12, December 2017, pp. 2524-2535.
IEEE DOI
1712
Computed tomography, Convolution, Decoding, Feature extraction,
Image reconstruction, X-ray imaging, Low-dose CT, auto-encoder,
residual neural network
BibRef
Shan, H.,
Zhang, Y.,
Yang, Q.,
Kruger, U.,
Kalra, M.K.,
Sun, L.,
Cong, W.,
Wang, G.,
3-D Convolutional Encoder-Decoder Network for Low-Dose CT via
Transfer Learning From a 2-D Trained Network,
MedImg(37), No. 6, June 2018, pp. 1522-1534.
IEEE DOI
1806
BibRef
Earlier:
Correction:
IEEE DOI
1812
Computed tomography, Linear programming,
Noise reduction, Solid modeling,
generative adversarial network, Lesions, Liver
BibRef
Wolterink, J.M.,
Leiner, T.,
Viergever, M.A.,
Igum, I.,
Generative Adversarial Networks for Noise Reduction in Low-Dose CT,
MedImg(36), No. 12, December 2017, pp. 2536-2545.
IEEE DOI
1712
Calcium, Computed tomography, Convolution, Generators,
Noise reduction, Training, Transforms, Coronary calcium scoring,
noise reduction
BibRef
Zheng, X.,
Ravishankar, S.,
Long, Y.,
Fessler, J.A.,
PWLS-ULTRA: An Efficient Clustering and Learning-Based Approach for
Low-Dose 3D CT Image Reconstruction,
MedImg(37), No. 6, June 2018, pp. 1498-1510.
IEEE DOI
1806
Computed tomography, Dictionaries, Image reconstruction,
Machine learning, Transforms,
statistical image reconstruction
BibRef
Gupta, H.,
Jin, K.H.,
Nguyen, H.Q.,
McCann, M.T.,
Unser, M.,
CNN-Based Projected Gradient Descent for Consistent CT Image
Reconstruction,
MedImg(37), No. 6, June 2018, pp. 1440-1453.
IEEE DOI
1806
Biomedical measurement, Computed tomography, Convex functions,
Image reconstruction, Inverse problems, Iterative methods,
low-dose computed tomography
BibRef
Kang, E.,
Chang, W.,
Yoo, J.,
Ye, J.C.,
Deep Convolutional Framelet Denosing for Low-Dose CT via Wavelet
Residual Network,
MedImg(37), No. 6, June 2018, pp. 1358-1369.
IEEE DOI
1806
Computed tomography, Convolution, Image reconstruction,
Machine learning, Neural networks, Noise reduction, X-ray imaging,
low-dose CT
BibRef
Yang, Q.S.[Qing-Song],
Yan, P.K.[Ping-Kun],
Zhang, Y.B.[Yan-Bo],
Yu, H.Y.[Heng-Yong],
Shi, Y.Y.[Yong-Yi],
Mou, X.Q.[Xuan-Qin],
Kalra, M.K.[Mannudeep K.],
Zhang, Y.[Yi],
Sun, L.[Ling],
Wang, G.[Ge],
Low-Dose CT Image Denoising Using a Generative Adversarial Network
With Wasserstein Distance and Perceptual Loss,
MedImg(37), No. 6, June 2018, pp. 1348-1357.
IEEE DOI
1806
Biomedical imaging, Computed tomography,
Image denoising, Image reconstruction, Machine learning, perceptual loss
BibRef
Han, Y.,
Ye, J.C.,
Framing U-Net via Deep Convolutional Framelets:
Application to Sparse-View CT,
MedImg(37), No. 6, June 2018, pp. 1418-1429.
IEEE DOI
1806
Computed tomography, Convolution, Image reconstruction,
Inverse problems, Machine learning, Matrix decomposition,
frame condition
BibRef
Zhang, Z.,
Liang, X.,
Dong, X.,
Xie, Y.,
Cao, G.,
A Sparse-View CT Reconstruction Method Based on Combination of
DenseNet and Deconvolution,
MedImg(37), No. 6, June 2018, pp. 1407-1417.
IEEE DOI
1806
Computed tomography, Deconvolution, Image reconstruction,
Neural networks, Reconstruction algorithms, Training,
deep learning
BibRef
Chen, H.,
Zhang, Y.,
Chen, Y.,
Zhang, J.,
Zhang, W.,
Sun, H.,
Lv, Y.,
Liao, P.,
Zhou, J.,
Wang, G.,
LEARN: Learned Experts: Assessment-Based Reconstruction Network for
Sparse-Data CT,
MedImg(37), No. 6, June 2018, pp. 1333-1347.
IEEE DOI
1806
Biomedical imaging, Computed tomography, Image reconstruction,
Iterative methods, Machine learning, Computed tomography (CT),
sparse-data CT
BibRef
Petrongolo, M.,
Zhu, L.,
Single-Scan Dual-Energy CT Using Primary Modulation,
MedImg(37), No. 8, August 2018, pp. 1799-1808.
IEEE DOI
1808
Computed tomography, Modulation, X-ray imaging,
Image reconstruction, Detectors, Geometry, Dual-energy CT,
iterative CT reconstruction
BibRef
He, J.,
Yang, Y.,
Wang, Y.,
Zeng, D.,
Bian, Z.,
Zhang, H.,
Sun, J.,
Xu, Z.,
Ma, J.,
Optimizing a Parameterized Plug-and-Play ADMM for Iterative Low-Dose
CT Reconstruction,
MedImg(38), No. 2, February 2019, pp. 371-382.
IEEE DOI
1902
Image reconstruction, Computed tomography, Optimization,
Biomedical imaging, Machine learning, X-ray imaging, Low-dose CT,
deep learning
BibRef
Cheng, L.[Lu],
Zhang, Y.K.[Yuan-Ke],
Song, Y.[Yun],
Li, C.[Chen],
Guo, D.S.[Dao-Shun],
Low-Dose CT Image Restoration Based on Adaptive Prior Feature Matching
and Nonlocal Means,
IJIG(19), No. 3 2019, pp. 1950017.
DOI Link
1908
BibRef
Du, W.,
Chen, H.,
Liao, P.,
Yang, H.,
Wang, G.,
Zhang, Y.,
Visual Attention Network for Low-Dose CT,
SPLetters(26), No. 8, August 2019, pp. 1152-1156.
IEEE DOI
1908
computerised tomography, image denoising, image reconstruction,
image restoration, learning (artificial intelligence),
generative adversarial network
BibRef
Hong, S.G.[Shang-Guan],
Zhang, X.[Xiong],
Cui, X.Y.[Xue-Ying],
Liu, Y.[Yi],
Zhang, Q.[Quan],
Gui, Z.G.[Zhi-Guo],
Sinogram restoration for low-dose X-ray computed tomography using
regularized Perona-Malik equation with intuitionistic fuzzy entropy,
SIViP(13), No. 8, November 2019, pp. 1511-1519.
Springer DOI
1911
BibRef
Bao, P.,
Xia, W.,
Yang, K.,
Chen, W.,
Chen, M.,
Xi, Y.,
Niu, S.,
Zhou, J.,
Zhang, H.,
Sun, H.,
Wang, Z.,
Zhang, Y.,
Convolutional Sparse Coding for Compressed Sensing CT Reconstruction,
MedImg(38), No. 11, November 2019, pp. 2607-2619.
IEEE DOI
1911
Image reconstruction, Computed tomography, Convolutional codes,
Dictionaries, TV, Image coding, Compressed sensing,
convolutional sparse coding
BibRef
Yin, X.,
Zhao, Q.,
Liu, J.,
Yang, W.,
Yang, J.,
Quan, G.,
Chen, Y.,
Shu, H.,
Luo, L.,
Coatrieux, J.,
Domain Progressive 3D Residual Convolution Network to Improve
Low-Dose CT Imaging,
MedImg(38), No. 12, December 2019, pp. 2903-2913.
IEEE DOI
1912
Computed tomography, Image reconstruction, X-ray imaging,
Convolution, Attenuation,
artifacts reduction
BibRef
Ye, S.,
Ravishankar, S.,
Long, Y.,
Fessler, J.A.,
SPULTRA: Low-Dose CT Image Reconstruction With Joint Statistical and
Learned Image Models,
MedImg(39), No. 3, March 2020, pp. 729-741.
IEEE DOI
2004
Image reconstruction, Computed tomography, Data models, Transforms,
X-ray imaging, Dictionaries,
machine learning
BibRef
Zheng, X.,
Lu, Z.,
Ravishankar, S.,
Long, Y.,
Fessier, J.A.,
Low dose CT image reconstruction with learned sparsifying transform,
IVMSP16(1-5)
IEEE DOI
1608
Computed tomography
BibRef
Tao, X.,
Zhang, H.,
Wang, Y.,
Yan, G.,
Zeng, D.,
Chen, W.,
Ma, J.,
VVBP-Tensor in the FBP Algorithm: Its Properties and Application in
Low-Dose CT Reconstruction,
MedImg(39), No. 3, March 2020, pp. 764-776.
IEEE DOI
2004
Tensors, Computed tomography, Image reconstruction, Sorting,
Image restoration, Singular value decomposition,
sorting
BibRef
Islam, M.S.[Md. Shafiqul],
Islam, R.[Rafiqul],
Generalized Gaussian model-based reconstruction method of computed
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SIViP(14), No. 3, April 2020, pp. 547-555.
WWW Link.
2004
BibRef
Fan, F.,
Shan, H.,
Kalra, M.K.,
Singh, R.,
Qian, G.,
Getzin, M.,
Teng, Y.,
Hahn, J.,
Wang, G.,
Quadratic Autoencoder (Q-AE) for Low-Dose CT Denoising,
MedImg(39), No. 6, June 2020, pp. 2035-2050.
IEEE DOI
2006
Deep learning, quadratic neurons, autoencoder, low-dose CT
BibRef
Li, M.,
Hsu, W.,
Xie, X.,
Cong, J.,
Gao, W.,
SACNN: Self-Attention Convolutional Neural Network for Low-Dose CT
Denoising With Self-Supervised Perceptual Loss Network,
MedImg(39), No. 7, July 2020, pp. 2289-2301.
IEEE DOI
2007
Computed tomography, Noise reduction,
Image reconstruction, Feature extraction,
perceptual loss
BibRef
Li, D.Y.[Dan-Yang],
Zeng, D.[Dong],
Li, S.[Sui],
Ge, Y.S.[Yong-Shuai],
Bian, Z.Y.[Zhao-Ying],
Huang, J.[Jing],
Ma, J.H.[Jian-Hua],
MDM-PCCT: Multiple Dynamic Modulations for High-Performance Spectral
PCCT Imaging,
MedImg(39), No. 11, November 2020, pp. 3630-3642.
IEEE DOI
2011
Photon counting computed tomography.
Modulation, Detectors, Computed tomography, Energy measurement,
Image reconstruction, Photonics,
material decomposition
BibRef
Zhao, C.,
Martin, T.,
Shao, X.,
Alger, J.R.,
Duddalwar, V.,
Wang, D.J.J.,
Low Dose CT Perfusion With K-Space Weighted Image Average (KWIA),
MedImg(39), No. 12, December 2020, pp. 3879-3890.
IEEE DOI
2012
Computed tomography, Signal to noise ratio, Electron tubes,
Spatial resolution,
image enhancement
BibRef
Perelli, A.[Alessandro],
Lexa, M.[Michael],
Can, A.[Ali],
Davies, M.E.[Mike E.],
Compressive Computed Tomography Reconstruction through Denoising
Approximate Message Passing,
SIIMS(13), No. 4, 2020, pp. 1860-1897.
DOI Link
2012
BibRef
Chen, D.D.[Dong-Dong],
Tachella, J.[Julián],
Davies, M.E.[Mike E.],
Equivariant Imaging: Learning Beyond the Range Space,
ICCV21(4359-4368)
IEEE DOI
2203
Training, Deep learning, Codes, Inverse problems,
Computed tomography, Imaging, Low-level and physics-based vision,
Transfer/Low-shot/Semi/Unsupervised Learning
BibRef
Sanders, T.,
Dwyer, C.,
Inpainting Versus Denoising for Dose Reduction in Scanning-Beam
Microscopies,
IP(29), 2020, pp. 351-359.
IEEE DOI
1910
compressed sensing, Gaussian noise, image denoising,
image restoration, image sampling, dose reduction,
maximum a posteriori estimation
BibRef
Zhang, H.,
Liu, B.,
Yu, H.,
Dong, B.,
MetaInv-Net: Meta Inversion Network for Sparse View CT Image
Reconstruction,
MedImg(40), No. 2, February 2021, pp. 621-634.
IEEE DOI
2102
Computed tomography, Image reconstruction, Heuristic algorithms,
Analytical models, Computational modeling,
sparse view CT
BibRef
Cho, S.H.[Sang-Hoon],
Lee, S.Y.[Seo-Young],
Lee, J.H.[Jong-Ha],
Lee, D.Y.[Dongh-Yeon],
Kim, H.Y.[Hyo-Yi],
Ryu, J.H.[Jong-Hyun],
Jeong, K.H.[Kil-Hwan],
Kim, K.G.[Kyu-Gyum],
Yoon, K.H.[Kwon-Ha],
Cho, S.R.[Seung-Ryong],
A Novel Low-Dose Dual-Energy Imaging Method for a Fast-Rotating
Gantry-Type CT Scanner,
MedImg(40), No. 3, March 2021, pp. 1007-1020.
IEEE DOI
2103
Computed tomography, Image reconstruction, Imaging, X-ray imaging,
Filtering algorithms, Noise reduction, Imaging phantoms,
CT image Reconstruction
BibRef
Xiang, J.X.[Jin-Xi],
Dong, Y.G.[Yong-Gui],
Yang, Y.J.[Yun-Jie],
FISTA-Net: Learning a Fast Iterative Shrinkage Thresholding Network
for Inverse Problems in Imaging,
MedImg(40), No. 5, May 2021, pp. 1329-1339.
IEEE DOI
2105
Imaging, Inverse problems, Deep learning, Data models,
Thresholding (Imaging), Iterative algorithms,
sparse-view CT
BibRef
Zhou, B.[Bo],
Zhou, S.K.[S. Kevin],
Duncan, J.S.[James S.],
Liu, C.[Chi],
Limited View Tomographic Reconstruction Using a Cascaded Residual
Dense Spatial-Channel Attention Network With Projection Data Fidelity
Layer,
MedImg(40), No. 7, July 2021, pp. 1792-1804.
IEEE DOI
2107
Image reconstruction, Tomography, Computed tomography, Geometry,
Detectors, Fans, X-ray imaging, Tomographic reconstruction,
sparse view
BibRef
Wang, C.[Chao],
Tao, M.[Min],
Nagy, J.G.[James G.],
Lou, Y.F.[Yi-Fei],
Limited-Angle CT Reconstruction via the L_1/L_2 Minimization,
SIIMS(14), No. 2, 2021, pp. 749-777.
DOI Link
2107
BibRef
Bubba, T.A.[Tatiana A.],
Galinier, M.[Mathilde],
Lassas, M.[Matti],
Prato, M.[Marco],
Ratti, L.[Luca],
Siltanen, S.[Samuli],
Deep Neural Networks for Inverse Problems with Pseudodifferential
Operators: An Application to Limited-Angle Tomography,
SIIMS(14), No. 2, 2021, pp. 470-505.
DOI Link
2107
BibRef
Sasmal, P.[Pradip],
Theeda, P.[Prasad],
Jampana, P.[Phanindra],
Sastry, C.S.[Challa S.],
Nullspace Property for Optimality of Minimum Frame Angle Under
Invertible Linear Operators,
SPLetters(28), 2021, pp. 1928-1932.
IEEE DOI
2110
Coherence, Matching pursuit algorithms, Sparse matrices,
Image reconstruction, Tomography, Standards,
semidefinite programming
BibRef
Theeda, P.[Prasad],
Kumar, P.U.P.[P. U. Praveen],
Sastry, C.S.,
Jampana, P.V.,
Optimization of Low-Dose Tomography via Binary Sensing Matrices,
IWCIA15(337-351).
Springer DOI
1601
BibRef
Zhang, Y.K.[Yi-Kun],
Hu, D.L.[Dian-Lin],
Zhao, Q.L.[Qian-Long],
Quan, G.T.[Guo-Tao],
Liu, J.[Jin],
Liu, Q.G.[Qie-Geng],
Zhang, Y.[Yi],
Coatrieux, G.[Gouenou],
Chen, Y.[Yang],
Yu, H.Y.[Heng-Yong],
CLEAR: Comprehensive Learning Enabled Adversarial Reconstruction for
Subtle Structure Enhanced Low-Dose CT Imaging,
MedImg(40), No. 11, November 2021, pp. 3089-3101.
IEEE DOI
2111
Image reconstruction, Computed tomography,
Generative adversarial networks, Generators, X-ray imaging,
one-step reconstruction
BibRef
Xia, W.J.[Wen-Jun],
Lu, Z.X.[Ze-Xin],
Huang, Y.Q.[Yong-Qiang],
Liu, Y.[Yan],
Chen, H.[Hu],
Zhou, J.[Jiliu],
Zhang, Y.[Yi],
CT Reconstruction With PDF: Parameter-Dependent Framework for Data
From Multiple Geometries and Dose Levels,
MedImg(40), No. 11, November 2021, pp. 3065-3076.
IEEE DOI
2111
Geometry, Image reconstruction, Computed tomography,
Reconstruction algorithms, Feature extraction, Training, radiation dose
BibRef
Wu, W.W.[Wei-Wen],
Hu, D.[Dianlin],
Niu, C.[Chuang],
Yu, H.Y.[Heng-Yong],
Vardhanabhuti, V.[Varut],
Wang, G.[Ge],
DRONE: Dual-Domain Residual-based Optimization NEtwork for
Sparse-View CT Reconstruction,
MedImg(40), No. 11, November 2021, pp. 3002-3014.
IEEE DOI
2111
Image reconstruction, Computed tomography, Imaging,
Biomedical measurement, Reconstruction algorithms,
compressed sensing
BibRef
Xia, W.J.[Wen-Jun],
Lu, Z.X.[Ze-Xin],
Huang, Y.Q.[Yong-Qiang],
Shi, Z.Q.[Zuo-Qiang],
Liu, Y.[Yan],
Chen, H.[Hu],
Chen, Y.[Yang],
Zhou, J.[Jiliu],
Zhang, Y.[Yi],
MAGIC: Manifold and Graph Integrative Convolutional Network for
Low-Dose CT Reconstruction,
MedImg(40), No. 12, December 2021, pp. 3459-3472.
IEEE DOI
2112
Manifolds, Image reconstruction, Computed tomography, Convolution,
Feature extraction, X-ray imaging,
semi-supervised learning
BibRef
Zhang, X.[Xiong],
Han, Z.F.[Ze-Fang],
Hong, S.G.[Shang-Guan],
Han, X.L.[Xing-Long],
Cui, X.Y.[Xue-Ying],
Wang, A.H.[An-Hong],
Artifact and Detail Attention Generative Adversarial Networks for
Low-Dose CT Denoising,
MedImg(40), No. 12, December 2021, pp. 3901-3918.
IEEE DOI
2112
Feature extraction, Noise reduction, Image edge detection,
Generators, Generative adversarial networks, Convolution,
Res2Net discriminator
BibRef
Bera, S.[Sutanu],
Biswas, P.K.[Prabir Kumar],
Noise Conscious Training of Non Local Neural Network Powered by Self
Attentive Spectral Normalized Markovian Patch GAN for Low Dose CT
Denoising,
MedImg(40), No. 12, December 2021, pp. 3663-3673.
IEEE DOI
2112
Computed tomography, Noise reduction, Training,
Image reconstruction, Biomedical imaging, Convolution,
neighbourhood similarity in CT
BibRef
Chen, C.Y.[Chang-Yu],
Xing, Y.X.[Yu-Xiang],
Gao, H.[Hewei],
Zhang, L.[Li],
Chen, Z.Q.[Zhi-Qiang],
Sam's Net: A Self-Augmented Multistage Deep-Learning Network for
End-to-End Reconstruction of Limited Angle CT,
MedImg(41), No. 10, October 2022, pp. 2912-2924.
IEEE DOI
2210
Image reconstruction, Optimization, Computed tomography, Training,
Minimization, Iterative methods, Artificial neural networks,
online augmentation
BibRef
Zhou, B.[Bo],
Chen, X.C.[Xiong-Chao],
Xie, H.D.[Hui-Dong],
Zhou, S.K.[S. Kevin],
Duncan, J.S.[James S.],
Liu, C.[Chi],
DuDoUFNet: Dual-Domain Under-to-Fully-Complete Progressive
Restoration Network for Simultaneous Metal Artifact Reduction and
Low-Dose CT Reconstruction,
MedImg(41), No. 12, December 2022, pp. 3587-3599.
IEEE DOI
2212
Image restoration, Computed tomography, Image reconstruction,
Metals, Implants, Image quality, X-ray imaging, Low-dose CT,
progressive restoration network
BibRef
Lu, Z.X.[Ze-Xin],
Xia, W.J.[Wen-Jun],
Huang, Y.Q.[Yong-Qiang],
Hou, M.Z.[Ming-Zheng],
Chen, H.[Hu],
Zhou, J.[Jiliu],
Shan, H.M.[Hong-Ming],
Zhang, Y.[Yi],
M3NAS: Multi-Scale and Multi-Level Memory-Efficient Neural
Architecture Search for Low-Dose CT Denoising,
MedImg(42), No. 3, March 2023, pp. 850-863.
IEEE DOI
2303
Noise reduction, Computed tomography, Network architecture,
Feature extraction, Task analysis, Image reconstruction, denoising
BibRef
Xia, W.J.[Wen-Jun],
Shan, H.M.[Hong-Ming],
Wang, G.[Ge],
Zhang, Y.[Yi],
Physics-/Model-Based and Data-Driven Methods for Low-Dose Computed
Tomography: A survey,
SPMag(40), No. 2, March 2023, pp. 89-100.
IEEE DOI
2303
Physics, Image quality, Deep learning, Technological innovation,
Computed tomography, Computational modeling, Noise reduction
BibRef
Yang, F.Q.[Fu-Qiang],
Zhang, D.H.[Ding-Hua],
Huang, K.D.[Kui-Dong],
Yang, Y.[Yao],
Li, Z.X.[Zhi-Xiang],
Complex artefact suppression for sparse reconstruction based on
compensation approach in X-ray computed tomography,
IET-IPR(17), No. 4, 2023, pp. 1291-1306.
DOI Link
2303
computed tomography, image reconstruction,
non-destructive testing, X-ray imaging
BibRef
Shen, J.[Jinbo],
Luo, M.T.[Meng-Ting],
Liu, H.[Han],
Liao, P.X.[Pei-Xi],
Chen, H.[Hu],
Zhang, Y.[Yi],
MLF-IOSC: Multi-Level Fusion Network With Independent Operation
Search Cell for Low-Dose CT Denoising,
MedImg(42), No. 4, April 2023, pp. 1145-1158.
IEEE DOI
2304
Computed tomography, Noise reduction, Convolution,
Computer architecture, Laplace equations, Image reconstruction,
Laplacian
BibRef
Yang, L.T.[Liu-Tao],
Li, Z.N.[Zhong-Nian],
Ge, R.J.[Rong-Jun],
Zhao, J.Y.[Jun-Yong],
Si, H.P.[Hai-Peng],
Zhang, D.Q.[Dao-Qiang],
Low-Dose CT Denoising via Sinogram Inner-Structure Transformer,
MedImg(42), No. 4, April 2023, pp. 910-921.
IEEE DOI
2304
Noise reduction, Transformers, Computed tomography,
Image reconstruction, Imaging, Periodic structures,
sinogram inner-structure
BibRef
Li, K.[Ke],
Chen, J.R.[Joshua Ray],
Feng, M.[Mang],
Construction of a Nearly Unbiased Statistical Estimator of Sinogram
to Address CT Number Bias Issues in Low-Dose Photon Counting CT,
MedImg(42), No. 6, June 2023, pp. 1846-1858.
IEEE DOI
2306
Computed tomography, Imaging, Detectors, Photonics, Transforms,
X-ray imaging, Physics, Photon-counting CT, low-dose CT, spectral CT,
material decomposition
BibRef
Niu, C.[Chuang],
Li, M.Z.[Meng-Zhou],
Fan, F.L.[Feng-Lei],
Wu, W.W.[Wei-Wen],
Guo, X.D.[Xiao-Dong],
Lyu, Q.[Qing],
Wang, G.[Ge],
Noise Suppression With Similarity-Based Self-Supervised Deep Learning,
MedImg(42), No. 6, June 2023, pp. 1590-1602.
IEEE DOI
2306
Noise reduction, Computed tomography, Noise measurement, Training,
Photonics, Image reconstruction, Image denoising,
photon-counting CT denoising
BibRef
Islam, M.S.[Md. Shafiqul],
Islam, R.[Rafiqul],
A Critical Survey on Developed Reconstruction Algorithms for Computed
Tomography Imaging from a Limited Number of Projections,
IJIG(23), No. 4 2023, pp. 2350026.
DOI Link
2308
BibRef
Li, M.[Ming],
Wang, J.P.[Ji-Ping],
Chen, Y.[Yang],
Tang, Y.F.[Yu-Fei],
Wu, Z.Y.[Zhong-Yi],
Qi, Y.J.[Yu-Jin],
Jiang, H.[Haochuan],
Zheng, J.[Jian],
Tsui, B.M.W.[Benjamin M. W.],
Low-Dose CT Image Synthesis for Domain Adaptation Imaging Using a
Generative Adversarial Network With Noise Encoding Transfer Learning,
MedImg(42), No. 9, September 2023, pp. 2616-2630.
IEEE DOI
2310
BibRef
Han, Z.[Zefang],
Shangguan, H.[Hong],
Zhang, X.[Xiong],
Cui, X.Y.[Xue-Ying],
Wang, Y.[Yue],
A coarse-to-fine multi-scale feature hybrid low-dose CT denoising
network,
SP:IC(118), 2023, pp. 117009.
Elsevier DOI
2310
Low-dose CT, Image denoising, Generative adversarial networks,
Self-calibration convolution, Multi-resolution inception discriminator
BibRef
Wu, Q.Y.[Qian-Yu],
Ji, X.[Xu],
Gu, Y.[Yunbo],
Xiang, J.[Jun],
Quan, G.[Guotao],
Li, B.[Baosheng],
Zhu, J.[Jian],
Coatrieux, G.[Gouenou],
Coatrieux, J.L.[Jean-Louis],
Chen, Y.[Yang],
Unsharp Structure Guided Filtering for Self-Supervised Low-Dose CT
Imaging,
MedImg(42), No. 11, November 2023, pp. 3283-3294.
IEEE DOI
2311
BibRef
Gao, Y.F.[Yong-Feng],
Tan, J.X.[Jia-Xing],
Shi, Y.Y.[Yong-Yi],
Zhang, H.[Hao],
Lu, S.[Siming],
Gupta, A.[Amit],
Li, H.[Haifang],
Reiter, M.[Michael],
Liang, Z.R.[Zheng-Rong],
Machine Learned Texture Prior From Full-Dose CT Database via
Multi-Modality Feature Selection for Bayesian Reconstruction of
Low-Dose CT,
MedImg(42), No. 11, November 2023, pp. 3129-3139.
IEEE DOI
2311
BibRef
Gao, Q.[Qi],
Li, Z.L.[Zi-Long],
Zhang, J.P.[Jun-Ping],
Zhang, Y.[Yi],
Shan, H.M.[Hong-Ming],
CoreDiff: Contextual Error-Modulated Generalized Diffusion Model for
Low-Dose CT Denoising and Generalization,
MedImg(43), No. 2, February 2024, pp. 745-759.
IEEE DOI Code:
WWW Link.
2402
Computed tomography, Noise reduction, Degradation,
Image restoration, Diffusion processes, Context modeling,
one-shot learning
BibRef
Li, X.[Xing],
Jing, K.[Kaili],
Yang, Y.[Yan],
Wang, Y.B.[Yong-Bo],
Ma, J.H.[Jian-Hua],
Zheng, H.R.[Hai-Rong],
Xu, Z.B.[Zong-Ben],
Noise-Generating and Imaging Mechanism Inspired Implicit
Regularization Learning Network for Low Dose CT Reconstrution,
MedImg(43), No. 5, May 2024, pp. 1677-1689.
IEEE DOI
2405
Image reconstruction, Computed tomography, Optimization,
Data models, Noise measurement, Computational modeling, deep learning
BibRef
Yang, C.[Chun],
Sheng, D.[Dian],
Yang, B.[Bo],
Zheng, W.F.[Wen-Feng],
Liu, C.[Chao],
A Dual-Domain Diffusion Model for Sparse-View CT Reconstruction,
SPLetters(31), 2024, pp. 1279-1283.
IEEE DOI
2405
Computed tomography, Image reconstruction, Task analysis, Degradation,
Refining, Pipelines, Training, Computed tomography (CT), sparse-view CT
BibRef
Huang, J.X.[Jia-Xin],
Chen, K.C.[Ke-Cheng],
Ren, Y.Z.[Ya-Zhou],
Sun, J.[Jiayu],
Pu, X.R.[Xiao-Rong],
Zhu, C.[Ce],
Cross-Domain Low-Dose CT Image Denoising With Semantic Preservation
and Noise Alignment,
MultMed(26), 2024, pp. 8771-8782.
IEEE DOI
2408
BibRef
Earlier: A1, A2, A4, A5, A3, Only:
Cross Domain Low-Dose CT Image Denoising With Semantic Information
Alignment,
ICIP22(4228-4232)
IEEE DOI
2211
Computed tomography, Semantics, Noise reduction, Training,
Image reconstruction, Image denoising, Frequency-domain analysis,
low-dose CT image.
Training, Task analysis, Deep learning
BibRef
Guo, Y.[Yu],
Wu, C.[Caiying],
Li, Y.X.[Ya-Xin],
Jin, Q.Y.[Qi-Yu],
Zeng, T.Y.[Tie-Yong],
Deep Inertia L_p Half-Quadratic Splitting Unrolling Network for
Sparse View CT Reconstruction,
SPLetters(31), 2024, pp. 2030-2034.
IEEE DOI
2408
Signal processing algorithms, Convergence, Computed tomography,
Deep learning, Image reconstruction, Noise, Gold, Inertial, L_p-norm,
unrolling
BibRef
Bera, S.[Sutanu],
Biswas, P.K.[Prabir Kumar],
Self Supervised Low Dose Computed Tomography Image Denoising Using
Invertible Network Exploiting Inter Slice Congruence,
WACV23(5603-5612)
IEEE DOI
2302
Training, Deep learning, Correlation, Computed tomography,
Noise reduction, Neural networks,
and un-supervised learning)
BibRef
Zhang, Y.S.[Yang-Song],
Roy, S.[Subhankar],
Lu, H.T.[Hong-Tao],
Ricci, E.[Elisa],
Lathuiličre, S.[Stéphane],
Cooperative Self-Training for Multi-Target Adaptive Semantic
Segmentation,
WACV23(5593-5602)
IEEE DOI
2302
Adaptation models, Uncertainty, Semantic segmentation,
Measurement uncertainty, Benchmark testing, Data models
BibRef
Lo˙en, E.[Estelle],
Dasnoy-Sumell, D.[Damien],
Macq, B.[Benoît],
3DCT Reconstruction from a Single X-Ray Projection Using
Convolutional Neural Network,
ICIP22(1111-1115)
IEEE DOI
2211
Training, Neural networks, Real-time systems, Safety,
Convolutional neural networks, Root mean square
BibRef
Nagare, M.[Madhuri],
Tang, J.[Jie],
Rahman, O.[Obaidullah],
Nett, B.[Brian],
Melnyk, R.[Roman],
Sauer, K.D.[Ken D.],
Bouman, C.A.[Charles A.],
A Noise Preserving Sharpening Filter for CT Image Enhancement,
ICIP22(2566-2570)
IEEE DOI
2211
Training, Image resolution, Computed tomography, Neural networks,
Filtering algorithms, Noise measurement, Kernel, Low-dose CT,
deep neural networks
BibRef
Ronen, R.[Roi],
Schechner, Y.Y.[Yoav Y.],
Eytan, E.[Eshkol],
4D Cloud Scattering Tomography,
ICCV21(5500-5509)
IEEE DOI
2203
CT for time-varying volumetric scattering object.
Correlation, Computed tomography, Clouds, Computational modeling,
Scattering, Data models, Stereo,
Low-level and physics-based vision
BibRef
Reed, A.W.[Albert W.],
Kim, H.[Hyojin],
Anirudh, R.[Rushil],
Mohan, K.A.[K. Aditya],
Champley, K.[Kyle],
Kang, J.[Jingu],
Jayasuriya, S.[Suren],
Dynamic CT Reconstruction from Limited Views with Implicit Neural
Representations and Parametric Motion Fields,
ICCV21(2238-2248)
IEEE DOI
2203
Codes, Computed tomography, Pipelines, Dynamics, Training data,
Optimization methods, Computational photography,
Low-level and physics-based vision
BibRef
Xu, L.[Lu],
Zhang, Y.W.[Yu-Wei],
Liu, Y.[Ying],
Wang, D.[Daoye],
Zhou, M.[Mu],
Ren, J.[Jimmy],
Wei, J.W.[Jing-Wei],
Ye, Z.X.[Zhao-Xiang],
Low-Dose CT Denoising Using A Structure-Preserving Kernel Prediction
Network,
ICIP21(1639-1643)
IEEE DOI
2201
Protocols, Computed tomography, Image processing, Noise reduction,
Imaging, Predictive models, Image Denoising,
Low-dose CT
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Ye, X.C.[Xin-Chen],
Xu, Y.Y.[Yu-Yao],
Xu, R.[Rui],
Kido, S.[Shoji],
Tomiyama, N.[Noriyuki],
Detail- Revealing Deep Low-Dose CT Reconstruction,
ICPR21(8789-8796)
IEEE DOI
2105
Measurement, Fuses, Computed tomography, Imaging,
Pattern recognition, Image reconstruction, Detail-revealing, noise
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Su, W.,
Qu, Y.,
Deng, C.,
Wang, Y.,
Zheng, F.,
Chen, Z.,
Enhance Generative Adversarial Networks By Wavelet Transform To
Denoise Low-Dose CT Images,
ICIP20(350-354)
IEEE DOI
2011
Computed tomography, Wavelet transforms, Noise reduction,
Generators, Generative adversarial networks,
image denoising
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Guo, Y.Y.[Yu-Yu],
Bi, L.[Lei],
Ahn, E.[Euijoon],
Feng, D.D.[David Dagan],
Wang, Q.[Qian],
Kim, J.M.[Jin-Man],
A Spatiotemporal Volumetric Interpolation Network for 4D Dynamic
Medical Image,
CVPR20(4725-4734)
IEEE DOI
2008
Interpolation, Spatiotemporal phenomena, Dynamics,
Biomedical optical imaging
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Li, Z.,
Ye, S.,
Long, Y.,
Ravishankar, S.,
SUPER Learning: A Supervised-Unsupervised Framework for Low-Dose CT
Image Reconstruction,
CLI19(3959-3968)
IEEE DOI
2004
computerised tomography, diagnostic radiography,
image reconstruction, iterative methods, transform learning
BibRef
Kamoshita, H.,
Shibata, T.,
Kitahara, D.,
Fujimoto, K.,
Hirabayashi, A.,
Low-Dose CT Reconstruction with Multiclass Orthogonal Dictionaries,
ICIP19(2055-2059)
IEEE DOI
1910
Low-dose CT, image reconstruction, sparse representation,
fast dictionary learning, clustering
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Pap, G.[Gergely],
Lékó, G.[Gábor],
Grósz, T.[Tamás],
A Reconstruction-Free Projection Selection Procedure for Binary
Tomography Using Convolutional Neural Networks,
ICIAR19(I:228-236).
Springer DOI
1909
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Sánchez, R.M.[Ricardo M.],
Mester, R.[Rudolf],
Kudryashev, M.[Mikhail],
Fast Cross Correlation for Limited Angle Tomographic Data,
SCIA19(415-426).
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1906
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Ghani, M.U.,
Karl, W.C.,
Deep Learning-Based Sinogram Completion for Low-Dose CT,
IVMSP18(1-5)
IEEE DOI
1809
Image reconstruction, Computed tomography, Training, Dictionaries,
Generators, Biomedical imaging, Low-dose,
Computed tomography
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Cormier, M.[Michael],
Lizotte, D.J.[Daniel J.],
Mann, R.[Richard],
Reconstruction of 3-D Density Functions from Few Projections:
Structural Assumptions for Graceful Degradation,
CRV15(147-154)
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1507
Computed tomography
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Denitiu, A.[Andreea],
Petra, S.[Stefania],
Schnörr, C.[Claudius],
Schnörr, C.[Christoph],
An Entropic Perturbation Approach to TV-Minimization for Limited-Data
Tomography,
DGCI14(262-274).
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1410
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Hassan, E.A.,
Kadah, Y.M.,
Study of compressed sensing application to low dose computed
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IPTA14(1-5)
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compressed sensing
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Barkan, O.[Oren],
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Dekel, S.[Shai],
Adaptive Compressed Tomography Sensing,
CVPR13(2195-2202)
IEEE DOI
1309
Adaptive Compressed Sensing; Computed Tomography; Low-dose CT; Ridgelets
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Hewett, R.J.[Russell J.],
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A Phase Field Method for Tomographic Reconstruction from Limited Data,
BMVC12(120).
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Improved kernel-based limited-view CT reconstruction VIA anisotropic
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ICIP11(1381-1384).
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1201
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Fast Iterative Adaptive Reconstruction in Low-Dose CT Imaging,
ICIP06(889-892).
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0610
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Solo, V.,
Regularisation of the limited data computed tomography problem via the
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ICIP95(II: 430-432).
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9510
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Chapter on Medical Applications, CAT, MRI, Ultrasound, Heart Models, Brain Models continues in
Statistical, Bayesian Tomographic Image Reconstruction .