21.8.4.4 Few Views, Limited Views, Low Dose, Tomographic Image Reconstruction

Chapter Contents (Back)
Reconstruction. Tomography. Low Dose CT.

Guan, H.Q.[Huai-Qun], Waleed Gaber, M., di Bianca, F.A., Zhu, Y.P.[Yun-Ping],
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Image reconstruction; Computerized tomography; Peeling; Algebraic reconstruction techniques; ART; Projections; Algorithm; Digital difference analyzer BibRef

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Discrete tomography; Stability; Image reconstruction; Uniqueness; Projections BibRef

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Image reconstruction; Radon transform; Projection moments; Image moments; Orthonormal polynomials BibRef

Dai, X.B., Shu, H.Z., Luo, L.M., Han, G.N., Coatrieux, J.L.,
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Discrete Radon transform; Discrete orthogonal moments; Projection moments; Image reconstruction BibRef

Han, X., Bian, J., Eaker, D.R., Kline, T.L., Sidky, E.Y., Ritman, E.L., Pan, X.C.[Xiao-Chuan],
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IEEE DOI 1103
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Ammari, H.[Habib], Tran, M.P.[Minh Phuong], Wang, H.[Han],
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Xu, Q., Yu, H.Y.[Heng-Yong], Mou, X.Q.[Xuan-Qin], Zhang, L., Hsieh, J., Wang, G.,
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IEEE DOI 1209
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Zhang, Y.B.[Yan-Bo], Mou, X.Q.[Xuan-Qin], Wang, G.[Ge], Yu, H.Y.[Heng-Yong],
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MedImg(36), No. 1, January 2017, pp. 142-154.
IEEE DOI 1701
Computed tomography BibRef

Pelt, D.M., Batenburg, K.J.,
Fast Tomographic Reconstruction From Limited Data Using Artificial Neural Networks,
IP(22), No. 12, 2013, pp. 5238-5251.
IEEE DOI 1312
computerised tomography BibRef

Feng, J.[Jun], Zhang, J.Z.[Jian-Zhou],
An adaptive dynamic combined energy minimization model for few-view computed tomography reconstruction,
IJIST(23), No. 1, March 2013, pp. 44-52.
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Zhang, Y.[Yi], Zhang, W.H.[Wei-Hua], Chen, H.[Hu], Yang, M.L.[Meng-Long], Li, T.Y.[Tai-Yong], Zhou, J.L.[Ji-Liu],
Few-view image reconstruction combining total variation and a high-order norm,
IJIST(23), No. 3, 2013, pp. 249-255.
DOI Link 1309
x-ray computed tomography BibRef

Zhang, Y.[Yi], Zhang, W.H.[Wei-Hua], Lei, Y.J.[Yin-Jie], Zhou, J.L.[Ji-Liu],
Few-view image reconstruction with fractional-order total variation,
JOSA-A(31), No. 5, May 2014, pp. 981-995.
DOI Link 1405
Image reconstruction techniques; X-ray imaging; Tomographic imaging BibRef

Sun, Y.[Yuli], Tao, J.[Jinxu],
Few views image reconstruction using alternating direction method via L0-norm minimization,
IJIST(24), No. 3, 2014, pp. 215-223.
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L0-norm optimization BibRef

Wang, L., Sixou, B., Peyrin, F.,
Binary Tomography Reconstructions With Stochastic Level-Set Methods,
SPLetters(22), No. 7, July 2015, pp. 920-924.
IEEE DOI 1412
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Earlier:
Binary tomography reconstructions of bone microstructure from few projections with stochastic level-set methods,
ICIP14(1778-1782)
IEEE DOI 1502
Bones BibRef

Momey, F., Denis, L., Burnier, C., Thiebaut, E., Becker, J.M., Desbat, L.,
Spline Driven: High Accuracy Projectors for Tomographic Reconstruction From Few Projections,
IP(24), No. 12, December 2015, pp. 4715-4725.
IEEE DOI 1512
computational complexity BibRef

Fang, R.[Ruogu], Zhang, S.T.[Shao-Ting], Chen, T.H.[Tsu-Han], Sanelli, P.C.[Pina C.],
Robust Low-Dose CT Perfusion Deconvolution via Tensor Total-Variation Regularization,
MedImg(34), No. 7, July 2015, pp. 1533-1548.
IEEE DOI 1507
Computed tomography BibRef

Fang, R.[Ruogu], Ni, M.[Ming], Huang, J.Z.[Jun-Zhou], Li, Q.[Qianmu], Li, T.[Tao],
Efficient 4D Non-local Tensor Total-Variation for Low-Dose CT Perfusion Deconvolution,
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Zhang, H., Han, H., Liang, Z., Hu, Y., Liu, Y., Moore, W., Ma, J., Lu, H.,
Extracting Information From Previous Full-Dose CT Scan for Knowledge-Based Bayesian Reconstruction of Current Low-Dose CT Images,
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Zhuge, X., 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], Balázs, P.[Péter],
Binary tomography reconstruction based on shape orientation,
PRL(79), No. 1, 2016, pp. 18-24.
Elsevier DOI 1608
Discrete tomography BibRef

Marceta, M.[Marina], Lukic, T.[Tibor],
Graph Cuts Based Tomography Enhanced by Shape Orientation,
IWCIA20(219-235).
Springer DOI 2009
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Nagy, B.[Benedek], Lukic, T.[Tibor],
Binary tomography on the isometric tessellation involving pixel shape orientation,
IET-IPR(14), No. 1, January 2020, pp. 25-30.
DOI Link 1912
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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., Išgum, 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
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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
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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 tomography image from fewer projections,
SIViP(14), No. 3, April 2020, pp. 547-555.
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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
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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
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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
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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
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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


Song, B.[Bowen], Shen, L.[Liyue], Xing, L.[Lei],
PINER: Prior-informed Implicit Neural Representation Learning for Test-time Adaptation in Sparse-view CT Reconstruction,
WACV23(1928-1937)
IEEE DOI 2302
Representation learning, Adaptation models, Computed tomography, Closed box, Training data, Noise measurement, Image reconstruction, Biomedical/healthcare/medicine 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

Huang, J.X.[Jia-Xin], Chen, K.C.[Ke-Cheng], Sun, J.[Jiayu], Pu, X.R.[Xiao-Rong], Ren, Y.Z.[Ya-Zhou],
Cross Domain Low-Dose CT Image Denoising With Semantic Information Alignment,
ICIP22(4228-4232)
IEEE DOI 2211
Training, Computed tomography, Semantics, Noise reduction, Image restoration, Task analysis, Image denoising, Deep learning 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 BibRef

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 BibRef

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 BibRef

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 BibRef

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 BibRef

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
BibRef

Sánchez, R.M.[Ricardo M.], Mester, R.[Rudolf], Kudryashev, M.[Mikhail],
Fast Cross Correlation for Limited Angle Tomographic Data,
SCIA19(415-426).
Springer DOI 1906
BibRef

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 BibRef

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)
IEEE DOI 1507
Computed tomography BibRef

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).
Springer DOI 1410
BibRef

Hassan, E.A., Kadah, Y.M.,
Study of compressed sensing application to low dose computed tomography data collection,
IPTA14(1-5)
IEEE DOI 1503
compressed sensing BibRef

Barkan, O.[Oren], Weill, J.[Jonathan], Averbuch, A.[Amir], Dekel, S.[Shai],
Adaptive Compressed Tomography Sensing,
CVPR13(2195-2202)
IEEE DOI 1309
Adaptive Compressed Sensing; Computed Tomography; Low-dose CT; Ridgelets BibRef

Hewett, R.J.[Russell J.], Jermyn, I.[Ian], Heath, M.[Michael], Kamalabadi, F.[Farzad],
A Phase Field Method for Tomographic Reconstruction from Limited Data,
BMVC12(120).
DOI Link 1301
BibRef

Feng, J.[Jun], Zhang, J.Z.[Jian-Zhou],
Improved kernel-based limited-view CT reconstruction VIA anisotropic diffusion,
ICIP11(1381-1384).
IEEE DOI 1201
BibRef

Cheng, L.[Lin], Chen, Y.Q.[Yun-Qiang], Fang, T.[Tong], Tyan, J.,
Fast Iterative Adaptive Reconstruction in Low-Dose CT Imaging,
ICIP06(889-892).
IEEE DOI 0610
BibRef

Solo, V.,
Regularisation of the limited data computed tomography problem via the boundary element method,
ICIP95(II: 430-432).
IEEE DOI 9510
BibRef

Chapter on Medical Applications, CAT, MRI, Ultrasound, Heart Models, Brain Models continues in
Statistical, Bayesian Tomographic Image Reconstruction .


Last update:Mar 16, 2024 at 20:36:19