Deep Learning-Based Multi-View Projection Synthesis Approach for Improving the Quality of Sparse-View CBCT in Image-Guided Radiotherapy

K. Bell, N. Licht, C. Rübe, Y. Dzierma, Image guidance and positioning accuracy in clinical practice: influence of positioning errors and imaging dose on the real dose distribution for head and neck cancer treatment, Radiation Oncology, 13 (2018) 1-13.

Article  Google Scholar 

N. Nabavizadeh, D.A. Elliott, Y. Chen, A.S. Kusano, T. Mitin, C.R. Thomas Jr, J.M. Holland, Image guided radiation therapy (IGRT) practice patterns and IGRT’s impact on workflow and treatment planning: Results from a national survey of American Society for Radiation Oncology members, International Journal of Radiation Oncology* Biology* Physics, 94 (2016) 850–857.

P. Alaei, E. Spezi, Imaging dose from cone beam computed tomography in radiation therapy, Physica Medica, 31 (2015) 647-658.

Article  PubMed  Google Scholar 

L. Zhou, S. Bai, Y. Zhang, X. Ming, Y. Zhang, J. Deng, Imaging dose, cancer risk and cost analysis in image-guided radiotherapy of cancers, Scientific Reports, 8 (2018) 10076.

Article  PubMed  PubMed Central  Google Scholar 

M.M. Rehani, E.R. Melick, R.M. Alvi, R. Doda Khera, S. Batool-Anwar, T.G. Neilan, M. Bettmann, Patients undergoing recurrent CT exams: assessment of patients with non-malignant diseases, reasons for imaging and imaging appropriateness, European radiology, 30 (2020) 1839–1846.

M. Brambilla, J. Vassileva, A. Kuchcinska, M.M. Rehani, Multinational data on cumulative radiation exposure of patients from recurrent radiological procedures: call for action, European radiology, 30 (2020) 2493-2501.

Article  PubMed  Google Scholar 

G.S. Ibbott, Patient doses from image-guided radiation therapy, Physica Medica, 72 (2020) 30-31.

Article  PubMed  Google Scholar 

G.X. Ding, P. Alaei, B. Curran, R. Flynn, M. Gossman, T.R. Mackie, M. Miften, R. Morin, X.G. Xu, T.C. Zhu, Image guidance doses delivered during radiotherapy: quantification, management, and reduction: report of the AAPM Therapy Physics Committee Task Group 180, Medical Physics, 45 (2018) e84-e99.

Article  PubMed  Google Scholar 

Y. Liu, H. Shangguan, Q. Zhang, H. Zhu, H. Shu, Z. Gui, Median prior constrained TV algorithm for sparse view low-dose CT reconstruction, Computers in Biology and Medicine, 60 (2015) 117-131.

Article  PubMed  Google Scholar 

X. Tao, H. Zhang, Y. Wang, G. Yan, D. Zeng, W. Chen, J. Ma, VVBP-tensor in the FBP algorithm: its properties and application in low-dose CT reconstruction, IEEE transactions on medical imaging, 39 (2019) 764-776.

Article  PubMed  Google Scholar 

E.Y. Sidky, X. Pan, Report on the AAPM deep-learning sparse-view CT grand challenge, Medical Physics, 49 (2022) 4935-4943.

Article  PubMed  Google Scholar 

G.-H. Chen, J. Tang, S. Leng, Prior image constrained compressed sensing (PICCS): a method to accurately reconstruct dynamic CT images from highly undersampled projection data sets, Medical Physics, 35 (2008) 660-663.

Article  PubMed  Google Scholar 

E.Y. Sidky, C.-M. Kao, X. Pan, Accurate image reconstruction from few-views and limited-angle data in divergent-beam CT, Journal of X-ray Science and Technology, 14 (2006) 119-139.

Google Scholar 

E.Y. Sidky, X. Pan, Image reconstruction in circular cone-beam computed tomography by constrained, total-variation minimization, Physics in Medicine and Biology, 53 (2008) 4777-4807.

Article  PubMed  PubMed Central  Google Scholar 

E.Y. Sidky, X. Pan, I.S. Reiser, R.M. Nishikawa, R.H. Moore, D.B. Kopans, Enhanced imaging of microcalcifications in digital breast tomosynthesis through improved image-reconstruction algorithms: Enhanced imaging of microcalcifications in digital breast tomosynthesis, Medical Physics, 36 (2009) 4920-4932.

Article  PubMed  PubMed Central  Google Scholar 

J. Bian, J.H. Siewerdsen, X. Han, E.Y. Sidky, J.L. Prince, C.A. Pelizzari, X. Pan, Evaluation of sparse-view reconstruction from flat-panel-detector cone-beam CT, Physics in Medicine and Biology, 55 (2010) 6575-6599.

Article  PubMed  PubMed Central  Google Scholar 

E.Y. Sidky, J.H. Jørgensen, X. Pan, Convex optimization problem prototyping for image reconstruction in computed tomography with the Chambolle–Pock algorithm, Physics in Medicine and Biology, 57 (2012) 3065-3091.

Article  PubMed  PubMed Central  Google Scholar 

X. Han, J. Bian, E.L. Ritman, E.Y. Sidky, X. Pan, Optimization-based reconstruction of sparse images from few-view projections, Physics in Medicine and Biology, 57 (2012) 5245-5273.

Article  PubMed  PubMed Central  Google Scholar 

Y. Song, W. Zhang, H. Zhang, Q. Wang, Q. Xiao, Z. Li, X. Wei, J. Lai, X. Wang, W. Li, Q. Zhong, P. Gong, R. Zhong, J. Zhao, Low-dose cone-beam CT (LD-CBCT) reconstruction for image-guided radiation therapy (IGRT) by three-dimensional dual-dictionary learning, Radiation Oncology, 15 (2020) 192.

Article  PubMed  PubMed Central  Google Scholar 

L.L. Geyer, U.J. Schoepf, F.G. Meinel, J.W. Nance Jr, G. Bastarrika, J.A. Leipsic, N.S. Paul, M. Rengo, A. Laghi, C.N. De Cecco, State of the art: iterative CT reconstruction techniques, Radiology, 276 (2015) 339-357.

Article  PubMed  Google Scholar 

G. Wang, J.C. Ye, B. De Man, Deep learning for tomographic image reconstruction, Nature Machine Intelligence, 2 (2020) 737-748.

Google Scholar 

F. Zhang, J. Liu, Y. Liu, X. Zhang, Research progress of deep learning in low-dose CT image denoising, Radiation Protection Dosimetry, 199 (2023) 337-346.

Article  PubMed  Google Scholar 

L.R. Koetzier, D. Mastrodicasa, T.P. Szczykutowicz, N.R. van der Werf, A.S. Wang, V. Sandfort, A.J. van der Molen, D. Fleischmann, M.J. Willemink, Deep learning image reconstruction for CT: technical principles and clinical prospects, Radiology, 306 (2023) e221257.

Article  PubMed  Google Scholar 

K.H. Jin, M.T. McCann, E. Froustey, M. Unser, Deep convolutional neural network for inverse problems in imaging, IEEE transactions on image processing, 26 (2017) 4509-4522.

Article  Google Scholar 

H. Chen, Y. Zhang, M.K. Kalra, F. Lin, Y. Chen, P. Liao, J. Zhou, G. Wang, Low-dose CT with a residual encoder-decoder convolutional neural network, IEEE transactions on medical imaging, 36 (2017) 2524-2535.

Article  PubMed  PubMed Central  Google Scholar 

Z. Zhang, X. Liang, X. Dong, Y. Xie, G. Cao, A Sparse-View CT Reconstruction Method Based on Combination of DenseNet and Deconvolution, IEEE Transactions on Medical Imaging, 37 (2018) 1407-1417.

Article  PubMed  Google Scholar 

Y. Han, J.C. Ye, Framing U-Net via deep convolutional framelets: Application to sparse-view CT, IEEE transactions on medical imaging, 37 (2018) 1418-1429.

Article  PubMed  Google Scholar 

D. Hu, J. Liu, T. Lv, Q. Zhao, Y. Zhang, G. Quan, J. Feng, Y. Chen, L. Luo, Hybrid-domain neural network processing for sparse-view CT reconstruction, IEEE Transactions on Radiation and Plasma Medical Sciences, 5 (2020) 88-98.

Article  Google Scholar 

M. Geng, X. Meng, J. Yu, L. Zhu, L. Jin, Z. Jiang, B. Qiu, H. Li, H. Kong, J. Yuan, Content-noise complementary learning for medical image denoising, IEEE transactions on medical imaging, 41 (2021) 407-419.

Article  Google Scholar 

Q. Yang, P. Yan, Y. Zhang, H. Yu, Y. Shi, X. Mou, M.K. Kalra, Y. Zhang, L. Sun, G. Wang, Low-Dose CT Image Denoising Using a Generative Adversarial Network With Wasserstein Distance and Perceptual Loss, IEEE Transactions on Medical Imaging, 37 (2018) 1348-1357.

Article  PubMed  PubMed Central  Google Scholar 

Z. Huang, X. Liu, R. Wang, J. Chen, P. Lu, Q. Zhang, C. Jiang, Y. Yang, X. Liu, H. Zheng, D. Liang, Z. Hu, Considering anatomical prior information for low-dose CT image enhancement using attribute-augmented Wasserstein generative adversarial networks, Neurocomputing, 428 (2021) 104-115.

Article  Google Scholar 

M. Chen, Y.-F. Pu, Y.-C. Bai, Low-dose CT image denoising using residual convolutional network with fractional TV loss, Neurocomputing, 452 (2021) 510-520.

Article  Google Scholar 

H. Li, X. Yang, S. Yang, D. Wang, G. Jeon, Transformer with double enhancement for low-dose CT denoising, IEEE journal of biomedical and health informatics, 27 (2022) 4660-4671.

Article  Google Scholar 

L. Zhu, Y. Han, X. Xi, H. Fu, S. Tan, M. Liu, S. Yang, C. Liu, L. Li, B. Yan, STEDNet: Swin transformer-based encoder–decoder network for noise reduction in low-dose CT, Medical Physics, 50 (2023) 4443-4458.

Article  PubMed  Google Scholar 

M. Jian, X. Yu, H. Zhang, C. Yang, SwinCT: feature enhancement based low-dose CT images denoising with swin transformer, Multimedia Systems, 30 (2024) 1.

Article  Google Scholar 

K. Liang, H. Yang, K. Kang, Y. Xing, Improve angular resolution for sparse-view CT with residual convolutional neural network, Medical Imaging 2018: Physics of Medical Imaging, SPIE, 2018, pp. 382-392.

H. Yuan, J. Jia, Z. Zhu, SIPID: A deep learning framework for sinogram interpolation and image denoising in low-dose CT reconstruction, 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), IEEE, 2018, pp. 1521-1524.

H. Lee, J. Lee, H. Kim, B. Cho, S. Cho, Deep-neural-network-based sinogram synthesis for sparse-view CT image reconstruction, IEEE Transactions on Radiation and Plasma Medical Sciences, 3 (2018) 109-119.

Article  Google Scholar 

J. Dong, J. Fu, Z. He, A deep learning reconstruction framework for X-ray computed tomography with incomplete data, PLoS ONE, 14 (2019) e0224426.

Article  PubMed 

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