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Cone-beam computed tomography (CBCT) offers advantages over conventional fan-beam CT in that it requires a shorter time and less exposure to obtain images.
An isotropic 3x3 image gradient operator,
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A Threshold Selection Method from Gray-Level Histograms,
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Flat-panel cone-beam computed-tomography for image-guided radiation therapy,
D. A. Jaffray, J. H. Siewerdsen, J. W. Wong, and A. A. Martinez, · 2002
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Image Quality Assessment: From Error Visibility to Structural Similarity,
Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, · 2004
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Assessment of residual error for online cone-beam CT-guided treatment of prostate cancer patients,
D. Letourneau, A. A. Martinez, D. Lockman, D. Yan, C. Vargas, G. Ivaldi, and J. Wong, · 2005
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Magnitude and clinical relevence of translational and rotational patient setup errors: a cone-beam CT study,
M. Gukenberger, J. Meyer, D. Vordermark, K. Baier, J. Wilbert, and M. Flentje, · 2006
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Image quality and stability of image-guided radiotherapy (IGRT) devices: a comparatives study,
M. Stock, M. Pasler, W. Birkfellner, P. Homolka, R. Poetter, and D. Georg, · 2009
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Multiscale registration of planning CT and daily cone beam CT images for adaptive radiation therapy,
D. Paquin, D. Levy, and L. Xing, · 2009
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Kilovoltage cone-beam CT: comparative dose and image quality evaluation in partial and full-angle scan protocols,
S. Kim, S. Yoo, F. F. Yin, E. Samei, and T. Yoshizumi, · 2010
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Spatially weighted mutual information image registration for image guided radiation therapy,
S. B. Park, F. C. Rhee, J. I. Monroe, and J. W. Sohn, · 2010
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Improved scatter correction using adaptive scatter kernel superposition,
M. Sun and J. M. Star-Lack, · 2010
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Elastix: a toolbox for intensity-based medical image registration,
S. Klein, M. Staring, K. Murphy, M. A. Viergever, and J. P. W. Pluim, · 2010
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Monte carlo study of the effects of system geometry and antiscatter grids on cone-beam CT scatter distribution,
A. Sisniega, W. Zbijewski, A. Badal, I. S. Kyprianou, J. W. Stayman, J. J. Vaquero, and J. H. Siewerdsen, · 2013
Cited alongside, same era.
Can radiomics features be reproducibly measured from CBCT images for patients with non-small cell lung cancer?,
X. Fave, D. Mackin, J. Yang, J. Zhang, D. Fried, P. Balter, D. Followill, D. Gomez, A. K. Jones, F. Stingo, J. Fontenot, and L. Court, · 2015
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U-Net: Convolutional Networks for Biomedical Image Segmentation,
O. Ronneberger, P. Fischer, and T. Brox, · 2015
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Chainer: a Next-Generation Open Source Framework for Deep Learning,
S. Tokui, K. Oono, S. Hido, and J. Clayton, · 2015
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Open Source Computer Vision Library,
Itseez, · 2015
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Unpaired Image-to-Image Translation Using Cycle-Consistent Adversarial Networks,
J. Zhu, T. Park, P. Isola, and A. A. Efros, · 2017
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Deep MR to CT Synthesis Using Unpaired Data,
J. M. Wolterink, A. M. Dinkla, M. H. F. Savenije, P. R. Seevinck, C. A. T. van den Berg, and I. Išgum, · 2017
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Improved Texture Networks: Maximizing Quality and Diversity in Feed-Forward Stylization and Texture Synthesis,
D. Ulyanov, A. Vedaldi, and V. S. Lempitsky, · 2017
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Improved Training of Wasserstein GANs,
I. Gulrajani, F. Ahmed, M. Arjovsky, V. Dumoulin, and A. C. Courville, · 2017
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No-reference/Blind Image Quality Assessment: A Survey,
S. Xu, S. Jiang, and W. Min, · 2017
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Assessing the impact of choosing different deformable registration algorithms on cone-beam CT enhancement by histogram matching,
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Image guided radiation therapy boost in combination with high-dose-rate intracavitary brachytherapy for the treatment of cervical cancer,
X. Wang, J. Li, P. Wang, K. Yuan, G. Yin, and B. Wan, · 2016
Cited alongside, same era.
Investigating deformable image registration and scatter correction for CBCT-based dose calculation in adaptive IMPT,
C. Kurz, F. Kamp, Y. Park, C. Zollner, S. Rit, D. Hansen, M. Podesta, G. C. Sharp, M. Li, M. Reiner, J. Hofmaier, S. Neppl, C. Thieke, R. Nijhuis, U. Ganswindt, C. Belka, B. A. Winey, K. Parodi, and G. Landry, · 2016
Cited alongside, same era.
Feature selection methodology for longitudinal cone-beam CT radiomics,
J. E. van Timmeren, R. T. H. Leijenaar, W. van Elmpt, B. Reymen, and P. Lambin, · 2017
Cited alongside, same era.
Optimal combination of anti-scatter grids and software correction for CBCT imaging,
U. Stankovic, L. S. Ploeger, M. van Herk, and J. J. Sonke, · 2017
Cited alongside, same era.
Fast shading correction for cone beam CT in radiation therapy via sparse sampling on planning CT,
L. Shi, T. Tsui, J. Wei, and L. Zhu, · 2017
Cited alongside, same era.
Image-to-image translation with conditional adversarial networks,
P. Isola, J. Zhu, T. Zhou, and A. A. Efros, · 2017
Cited alongside, same era.
Medical image Synthesis with context-aware generative adversarial networks,
D. Nie, R. Trullo, J. Lian, C. Petitjean, S. Ruan, Q. Wang, and D. Shen, · 2017
Cited alongside, same era.
H. S. Kidar and H. Azizi, · 2018
Later among the works it cites.
Image quality improvement in cone-beam CT using the super-resolution technique,
A. Oyama, S. Kumagai, N. Arai, T. Takata, Y. Saikawa, K. Shiraishi, T. Kobayashi, and J. Kotoku, · 2018
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Cone beam computed tomography image improvement using a deep convolutional neural network,
S. Kida, T. Nakamoto, M. Nakano, K. Nawa, A. Haga, J. Kotoku, H. Yamashita, and K. Nakagawa, · 2018
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Deep learning in radiology: an overview of the concepts and a survey of the state of the art,
M. Mazurowski, M. Buda, A. Saha, and M. Bashir, · 2018
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Improvement of image quality at CT and MRI using deep learning,
T. Higaki, Y. Nakamura, F. Tatsugami, T. Nakaura, and K. Awai, · 2018
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On the Effectiveness of Least Squares Generative Adversarial Networks,
X. Mao, Q. Li, H. Xie, R. Y. K. Lau, Z. Wang, and S. P. Smolley, · 2018
Later among the works it cites.
Generating synthesized computed tomography (CT) from cone-beam computed tomography (CBCT) using CycleGAN for adaptive radiation therapy,
X. Liang, L. Chen, D. Nguyen, Z. Zhou, X. Gu, M. Yang, J. Wang, and S. Jiang, · 2019
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