20.8.4.1 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],
CT reconstruction by using the MLS-ART technique and the KCD imaging system. I. Low-energy X-ray studies,
MedImg(18), No. 4, April 1999, pp. 355-358.
IEEE Top Reference. 0110
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Wang, T.J.[Tian J.], Sze, T.W.,
The image moment method for the limited range CT image reconstruction and pattern recognition,
PR(34), No. 11, November 2001, pp. 2145-2154.
Elsevier DOI 0108
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Herman, G.T.[Gabor T.],
A note on exact image reconstruction from a limited number of projections,
JVCIR(20), No. 1, January 2009, pp. 65-67.
Elsevier DOI 0804
Image reconstruction; Computerized tomography; Peeling; Algebraic reconstruction techniques; ART; Projections; Algorithm; Digital difference analyzer BibRef

Shu, H.Z., Zhou, J., Han, G.N., Luo, L.M., Coatrieux, J.L.,
Image reconstruction from limited range projections using orthogonal moments,
PR(40), No. 2, February 2007, pp. 670-680.
WWW Link. 0611
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.,
Reconstruction of tomographic images from limited range projections using discrete Radon transform and Tchebichef moments,
PR(43), No. 3, March 2010, pp. 1152-1164.
Elsevier DOI 1001
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],
Algorithm-Enabled Low-Dose Micro-CT Imaging,
MedImg(30), No. 3, March 2011, pp. 606-620.
IEEE DOI 1103
BibRef

Ammari, H.[Habib], Asch, M.[Mark], Bustos, L.G.[Lili Guadarrama], Jugnon, V.[Vincent], Kang, H.B.[Hyeon-Bae],
Transient Wave Imaging with Limited-View Data,
SIIMS(4), No. 4 2011, pp. 1097.
DOI Link 1112
Inverse problem, source from the image. BibRef

Ammari, H.[Habib], Tran, M.P.[Minh Phuong], Wang, H.[Han],
Shape Identification and Classification in Echolocation,
SIIMS(7), No. 3, 2014, pp. 1883-1905.
DOI Link 1410
BibRef

Xu, Q., Yu, H.Y.[Heng-Yong], Mou, X.Q.[Xuan-Qin], Zhang, L., Hsieh, J., Wang, G.,
Low-Dose X-ray CT Reconstruction via Dictionary Learning,
MedImg(31), No. 9, September 2012, pp. 1682-1697.
IEEE DOI 1209
BibRef

Zhang, Y.B.[Yan-Bo], Mou, X.Q.[Xuan-Qin], Wang, G.[Ge], Yu, H.Y.[Heng-Yong],
Tensor-Based Dictionary Learning for Spectral CT Reconstruction,
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.
DOI Link 1303
BibRef

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.
DOI Link 1408
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
BibRef
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,
MCV15(168-179).
Springer DOI 1608
BibRef

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,
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

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

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
Computed tomography, Gallium nitride, Linear programming, Noise reduction, Solid modeling, generative adversarial network 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, Gallium nitride, 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


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

Prasad, T.[Theeda], 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

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:Aug 16, 2018 at 18:22:30