21.8.7 Tomographic Image Generation, Cone-Beam, Fan-Beam, Helical, Spiral Reconstruction

Park, S., Kim, S., Yi, B., Hugo, G., Gach, H.M., Motai, Y.,
A Novel Method of Cone Beam CT Projection Binning Based on Image Registration,
MedImg(36), No. 8, August 2017, pp. 1733-1745.
IEEE DOI 1708
Computed tomography, Feature extraction, Image reconstruction, Image registration, Tracking, Trajectory, Tumors, Cone beam computed tomography (CBCT), image registration, projection binning, respiratory, signal BibRef

Lee, D., Lee, J., Kim, H., Lee, T., Soh, J., Park, M., Kim, C., Lee, Y.J., Cho, S.,
A Feasibility Study of Low-Dose Single-Scan Dual-Energy Cone-Beam CT in Many-View Under-Sampling Framework,
MedImg(36), No. 12, December 2017, pp. 2578-2587.
IEEE DOI 1712
Computed tomography, Detectors, Electron tubes, Filtering algorithms, Image reconstruction, X-ray imaging, low-dose CT BibRef

Liu, L., Li, X., Xiang, K., Wang, J., Tan, S.,
Low-Dose CBCT Reconstruction Using Hessian Schatten Penalties,
MedImg(36), No. 12, December 2017, pp. 2588-2599.
IEEE DOI 1712
Image edge detection, Image reconstruction, Imaging, Linear programming, Minimization, TV, staircase effect BibRef

Tilley, S., Jacobson, M., Cao, Q., Brehler, M., Sisniega, A., Zbijewski, W., Stayman, J.W.,
Penalized-Likelihood Reconstruction With High-Fidelity Measurement Models for High-Resolution Cone-Beam Imaging,
MedImg(37), No. 4, April 2018, pp. 988-999.
IEEE DOI 1804
Bones, Computed tomography, Correlation, Covariance matrices, Image reconstruction, Mathematical model, Noise measurement, trabecular bone BibRef

Malladi, S.P.K.[Sai Phani Kumar], Veduruparthi, B.K.[Bijju Kranthi], Mukherjee, J.[Jayanta], Das, P.P.[Partha Pratim], Chakrabarti, S.[Saswat], Mallick, I.[Indranil],
Robust 3D registration of CBCT images aggregating multiple estimates through random sampling,
PRL(108), 2018, pp. 8-14.
Elsevier DOI 1805
Mutual information, Sampling rate, 3D image registration BibRef

Chen, B., Xiang, K., Gong, Z., Wang, J., Tan, S.,
Statistical Iterative CBCT Reconstruction Based on Neural Network,
MedImg(37), No. 6, June 2018, pp. 1511-1521.
IEEE DOI 1806
Computed tomography, Image reconstruction, Iterative methods, Kernel, Neural networks, Spatial resolution, TV, CBCT, regularization term BibRef

Zhao, Y., Xu, J., Li, H., Zhang, P.,
Edge Information Diffusion-Based Reconstruction for Cone Beam Computed Laminography,
IP(27), No. 9, September 2018, pp. 4663-4675.
IEEE DOI 1807
automatic optical inspection, computerised tomography, image reconstruction, mechanical engineering computing, image reconstruction BibRef

Li, B., Shen, C., Chi, Y., Yang, M., Lou, Y., Zhou, L., Jia, X.,
Multienergy Cone-Beam Computed Tomography Reconstruction with a Spatial Spectral Nonlocal Means Algorithm,
SIIMS(11), No. 2, 2018, pp. 1205-1229.
DOI Link 1807
BibRef

Pei, Y., Yi, Y., Ma, G., Kim, T., Guo, Y., Xu, T., Zha, H.,
Spatially Consistent Supervoxel Correspondences of Cone-Beam Computed Tomography Images,
MedImg(37), No. 10, October 2018, pp. 2310-2321.
IEEE DOI 1810
Vegetation, Complexity theory, Computed tomography, Clustering algorithms, Measurement, Image registration, tree-pruning algorithm BibRef

Abdurahman, S., Frysch, R., Bismark, R., Melnik, S., Beuing, O., Rose, G.,
Beam Hardening Correction Using Cone Beam Consistency Conditions,
MedImg(37), No. 10, October 2018, pp. 2266-2277.
IEEE DOI 1810
Attenuation, Bones, X-ray imaging, Detectors, Computed tomography, Image reconstruction, Calibration, Cone beam computed tomography, Grangeat's fundamental relation BibRef

Pansiot, J.[Julien], Boyer, E.[Edmond],
CBCT of a Moving Sample From X-Rays and Multiple Videos,
MedImg(38), No. 2, February 2019, pp. 383-393.
IEEE DOI 1902
X-rays, Attenuation, Solid modeling, Image reconstruction, Imaging, Bayes methods, Radiography, video, Bayesian BibRef

Jiang, Z., Chen, Y., Zhang, Y., Ge, Y., Yin, F., Ren, L.,
Augmentation of CBCT Reconstructed From Under-Sampled Projections Using Deep Learning,
MedImg(38), No. 11, November 2019, pp. 2705-2715.
IEEE DOI 1911
Image reconstruction, Image edge detection, TV, Computed tomography, Biomedical imaging, Deep learning, imaging dose reduction BibRef

Pu, H., Gao, P., Liu, Y., Rong, J., Shi, F., Lu, H.,
Principal Component Analysis Based Dynamic Cone Beam X-Ray Luminescence Computed Tomography: A Feasibility Study,
MedImg(38), No. 12, December 2019, pp. 2891-2902.
IEEE DOI 1912
Principal component analysis, X-ray imaging, Probes, Image reconstruction, Optical imaging, X-ray luminescence computed tomography (XLCT) BibRef

Li, Y., Garrett, J.W., Li, K., Strother, C., Chen, G.,
An Enhanced SMART-RECON Algorithm for Time-Resolved C-Arm Cone-Beam CT Imaging,
MedImg(39), No. 6, June 2020, pp. 1894-1905.
IEEE DOI 2006
C-arm CBCT, time-resolved imaging, temporal resolution, image reconstruction BibRef

He, J., Wang, Y., Ma, J.,
Radon Inversion via Deep Learning,
MedImg(39), No. 6, June 2020, pp. 2076-2087.
IEEE DOI 2006
Radon transform, Radon inversion, computed tomography, image reconstruction, deep learning BibRef

Han, Y., Kim, J., Ye, J.C.,
Differentiated Backprojection Domain Deep Learning for Conebeam Artifact Removal,
MedImg(39), No. 11, November 2020, pp. 3571-3582.
IEEE DOI 2011
Neural networks, Computed tomography, Trajectory, Geometry, Deconvolution, Deep learning, Image reconstruction, spectral blending BibRef

Syben, C., Stimpel, B., Roser, P., Dörfler, A., Maier, A.,
Known Operator Learning Enables Constrained Projection Geometry Conversion: Parallel to Cone-Beam for Hybrid MR/X-Ray Imaging,
MedImg(39), No. 11, November 2020, pp. 3488-3498.
IEEE DOI 2011
Detectors, Magnetic resonance imaging, Geometry, X-ray imaging, Tomography, Image reconstruction, Known operator learning, MRI BibRef

Hayes, J.W.[John W.], Montoya, J.[Juan], Budde, A.[Adam], Zhang, C.Z.[Cheng-Zhu], Li, Y.[Yinsheng], Li, K.[Ke], Hsieh, J.[Jiang], Chen, G.H.[Guang-Hong],
High Pitch Helical CT Reconstruction,
MedImg(40), No. 11, November 2021, pp. 3077-3088.
IEEE DOI 2111
Image reconstruction, Computed tomography, Detectors, Lighting, Deep learning, X-ray imaging, image reconstruction BibRef

Zhi, S.H.[Shao-Hua], Kachelrieß, M.[Marc], Pan, F.[Fei], Mou, X.Q.[Xuan-Qin],
CycN-Net: A Convolutional Neural Network Specialized for 4D CBCT Images Refinement,
MedImg(40), No. 11, November 2021, pp. 3054-3064.
IEEE DOI 2111
Image reconstruction, Computed tomography, Logic gates, Image restoration, Imaging, Training, Correlation, spatiotemporal resolution BibRef

Wang, X.C.[Xian-Chao], Jiang, C.S.[Cheng-Shun],
New calculation method for exact length weighting factor in cone-beam computed tomography,
IJIST(31), No. 4, 2021, pp. 2136-2143.
DOI Link 2112
ART algorithm, CT, system matrix, weighting factor BibRef

Sun, D.[Diya], Zhang, Y.[Yungeng], Pei, Y.[Yuru], Li, P.X.[Pei-Xin], Nie, K.[Kaichen], Xu, T.[Tianmin], Wang, T.[Tianbing], Zha, H.B.[Hong-Bin],
Deep Volumetric Descriptor Learning for Dense Correspondence of Cone-Beam Computed Tomography via Spectral Maps,
MedImg(41), No. 8, August 2022, pp. 2157-2169.
IEEE DOI 2208
Shape, Feature extraction, Annotations, Neural networks, Image registration, Task analysis, spectral maps BibRef

Tivnan, M.[Matthew], Wang, W.Y.[Wen-Ying], Gang, G.[Grace], Stayman, J.W.[J. Webster],
Design Optimization of Spatial-Spectral Filters for Cone-Beam CT Material Decomposition,
MedImg(41), No. 9, September 2022, pp. 2399-2413.
IEEE DOI 2209
Computed tomography, Sensitivity, Imaging, Photonics, Detectors, Attenuation, Measurement, Cone-beam CT, design optimization, spectral CT BibRef

Yang, P.F.[Peng-Fei], Ge, X.[Xin], Tsui, T.[Tiffany], Liang, X.K.[Xiao-Kun], Xie, Y.Q.[Yao-Qin], Hu, Z.L.[Zhan-Li], Niu, T.Y.[Tian-Ye],
Four-Dimensional Cone Beam CT Imaging Using a Single Routine Scan via Deep Learning,
MedImg(42), No. 5, May 2023, pp. 1495-1508.
IEEE DOI 2305
Image reconstruction, Computed tomography, Tumors, Iterative methods, Imaging, Phase measurement, generative adversarial network BibRef

Zhang, Y.W.[Yi-Wen], Li, C.P.[Chuan-Pu], Dai, Z.H.[Zhen-Hui], Zhong, L.M.[Li-Ming], Wang, X.T.[Xue-Tao], Yang, W.[Wei],
Breath-Hold CBCT-Guided CBCT-to-CT Synthesis via Multimodal Unsupervised Representation Disentanglement Learning,
MedImg(42), No. 8, August 2023, pp. 2313-2324.
IEEE DOI 2308
Computed tomography, Motion artifacts, Subspace constraints, Radiation therapy, Generators, Breast cancer, Image synthesis, unsupervised learning BibRef

Mouchet, M.[Mélanie], Létang, J.M.[Jean Michel], Lesaint, J.[Jerome], Rit, S.[Simon],
Cone-Beam Pair-Wise Data Consistency Conditions in Helical CT,
MedImg(42), No. 10, October 2023, pp. 2853-2864.
IEEE DOI 2310
BibRef

Joseph, J.[Jiffy], Singh, A.[Aparajit], Pournami, P.N., Jayaraj, P.B., Puzhakkal, N.[Niyas],
Cone beam computed tomography enhancement using feature-embedded variational autoencoder with a perceptual loss function,
IJIST(33), No. 5, 2023, pp. 1767-1778.
DOI Link 2310
cone beam computed tomography, fan beam computed tomography, image-guided radiation therapy, perceptual loss function, variational autoencoder BibRef

Zhong, Y.C.[Yi-Cheng], Pei, Y.[Yuru], Nie, K.[Kaichen], Zhang, Y.[Yungeng], Xu, T.[Tianmin], Zha, H.B.[Hong-Bin],
Bi-Graph Reasoning for Masticatory Muscle Segmentation From Cone-Beam Computed Tomography,
MedImg(42), No. 12, December 2023, pp. 3690-3701.
IEEE DOI 2312
BibRef

Chen, X.[Xin], Wang, B.[Bin], Gao, Y.S.[Yong-Sheng],
Separated Fan-Beam Projection with Gaussian Convolution for Invariant and Robust Butterfly Image Retrieval,
PR(147), 2024, pp. 110083.
Elsevier DOI 2312
Butterfly image retrieval, fan-beam projection, patch pattern, texture feature, deep-learning feature, feature fusion BibRef

Su, T.[Ting], Zhu, J.[Jiongtao], Zhang, X.[Xin], Tan, Y.H.[Yu-Hang], Cui, H.[Han], Zeng, D.[Dong], Guo, J.C.[Jin-Chuan], Zheng, H.R.[Hai-Rong], Ma, J.H.[Jian-Hua], Liang, D.[Dong], Ge, Y.[Yongshuai],
Super Resolution Dual-Energy Cone-Beam CT Imaging With Dual-Layer Flat-Panel Detector,
MedImg(43), No. 2, February 2024, pp. 734-744.
IEEE DOI 2402
Imaging, Spatial resolution, Detectors, Computed tomography, Biomedical imaging, X-ray imaging, Data acquisition, CT image reconstruction BibRef

Li, Y.X.[Yun-Xiang], Shao, H.C.[Hua-Chieh], Liang, X.[Xiao], Chen, L.Y.[Li-Yuan], Li, R.Q.[Rui-Qi], Jiang, S.[Steve], Wang, J.[Jing], Zhang, Y.[You],
Zero-Shot Medical Image Translation via Frequency-Guided Diffusion Models,
MedImg(43), No. 3, March 2024, pp. 980-993.
IEEE DOI 2403
Computed tomography, Frequency-domain analysis, Medical diagnostic imaging, Planning, Imaging, Task analysis, cone-beam computed tomography BibRef

Qiao, J.H.[Jin-Hao], Liu, J.[Jiang], Yu, H.[Heng], Xiao, Y.[Yi], Yu, H.S.[Hong-Shan], Zheng, Y.[Yan], Li, S.[Sihan],
VAG: Voxel Attenuation Grid for Sparse-View CBCT Reconstruction,
ICIP24(2793-2799)
IEEE DOI Code:
WWW Link. 2411
Solid modeling, TV, Computed tomography, Noise, Neural networks, Mean square error methods, Sparse view CBCT reconstruction, Sparse view CBCT BibRef

Choi, Y.J.[Yong-Jin], Kwon, D.[Doeyoung], Baek, S.J.[Seung Jun],
Dual Domain Diffusion Guidance for 3D CBCT Metal Artifact Reduction,
WACV24(7950-7959)
IEEE DOI 2404
Solid modeling, Computed tomography, Memory management, Metals, Training data, Applications, Biomedical / healthcare / medicine. BibRef

Wang, Y.L.[Yan-Li], Chao, L.[Lianying], Shan, W.Q.[Wen-Qi], Zhang, H.[Haobo], Wang, Z.W.[Zhi-Wei], Li, Q.[Qiang],
Improving the Quality of Sparse-view Cone-beam Computed Tomography via Reconstruction-friendly Interpolation Network,
ACCV22(VI:86-100).
Springer DOI 2307
BibRef

Syben, C.[Christopher], Stimpel, B.[Bernhard], Lommen, J.[Jonathan], Würfl, T.[Tobias], Dörfler, A.[Arnd], Maier, A.[Andreas],
Deriving Neural Network Architectures Using Precision Learning: Parallel-to-Fan Beam Conversion,
GCPR18(503-517).
Springer DOI 1905
I.e. transformation of the parallel-beam MRI projections to fan-beam X-ray. BibRef

Wedekind, M., Kassubeck, M., Magnor, M.,
Real-Time High-Resolution Cone-Beam CT Using GPU-Based Multi-Resolution Sampling,
ICIP18(1163-1167)
IEEE DOI 1809
Image reconstruction, Graphics processing units, Computed tomography, Real-time systems, Image resolution, Indexes, gpu BibRef

Cui, J.J.[Jin-Jin], Qiang, Z.[Zhe],
Potts model for limited angle tomography,
ICIVC17(611-615)
IEEE DOI 1708
Computational modeling, Image reconstruction, Mathematical model, Minimization, Nickel, Tomography, Unified modeling language, L0, Potts, limited angle tomography, split Bregman, weight BibRef

Cui, J.J.[Jin-Jin], Zhao, Z.H.[Zhen-Hua],
Mumford-Shah-ATV functional for limited angle tomography,
ICIVC17(606-610)
IEEE DOI 1708
Algorithm design and analysis, Image edge detection, Image reconstruction, Image restoration, Minimization, TV, Tomography, Anisotropic TV, Mumford-Shah, TV, alternating optimization algorithm, limited, angle, tomography BibRef

Lam, W.Y.H., Ngan, H.Y.T., Wat, P.Y.P., Luk, H.W.K., Pow, E.H.N., Goto, T.K.,
Novel geometric coordination registration in cone-beam computed Tomogram,
AIPR14(1-6)
IEEE DOI 1504
computerised tomography BibRef