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During orthopaedic surgery, the inserting of metallic implants or screws are often performed under mobile C-arm systems.
“Iterative deblurring for ct metal artifact reduction,”
Ge Wang, Donald L Snyder, Joseph A O’Sullivan, and Michael W Vannier, · 1996
Earlier work this paper cites.
“Normalized metal artifact reduction (nmar) in computed tomography,”
Esther Meyer, Rainer Raupach, Michael Lell, Bernhard Schmidt, and Marc Kachelrieß, · 2010
Earlier work this paper cites.
“Metal artifact reduction in x-ray computed tomography (ct) by constrained optimization,”
Xiaomeng Zhang, Jing Wang, and Lei Xing, · 2011
Earlier work this paper cites.
“Frequency split metal artifact reduction (fsmar) in computed tomography,”
Esther Meyer, Rainer Raupach, Michael Lell, Bernhard Schmidt, and Marc Kachelrieß, · 2012
Earlier work this paper cites.
“The virtual skeleton database: an open access repository for biomedical research and collaboration,”
Michael Kistler, Serena Bonaretti, Marcel Pfahrer, Roman Niklaus, Philippe Büchler, et al., · 2013
Earlier work this paper cites.
“Conrad—a software framework for cone-beam imaging in radiology,”
Andreas Maier, Hannes G Hofmann, Martin Berger, Peter Fischer, Chris Schwemmer, Haibo Wu, Kerstin Müller, Joachim Hornegger, Jang-Hwan Choi, Christian Riess, et al., · 2013
Earlier work this paper cites.
“Current and novel techniques for metal artifact reduction at ct: practical guide for radiologists,”
Masaki Katsura, Jiro Sato, Masaaki Akahane, Akira Kunimatsu, Osamu Abe, et al., · 2018
Earlier work this paper cites.
“Convolutional neural network based metal artifact reduction in x-ray computed tomography,”
Yanbo Zhang and Hengyong Yu, · 2018
Cited alongside, same era.
“Generative mask pyramid network for ct/cbct metal artifact reduction with joint projection-sinogram correction,”
Haofu Liao, Wei-An Lin, Zhimin Huo, Levon Vogelsang, William J Sehnert, S Kevin Zhou, and Jiebo Luo, · 2019
Cited alongside, same era.
“Deep learning based metal inpainting in the projection domain: Initial results,”
Tristan M Gottschalk, Björn W Kreher, Holger Kunze, and Andreas Maier, · 2019
Cited alongside, same era.
“Score-based generative modeling through stochastic differential equations,”
Yang Song, Jascha Sohl-Dickstein, Diederik P Kingma, Abhishek Kumar, Stefano Ermon, and Ben Poole, · 2020
Cited alongside, same era.
“Denoising diffusion probabilistic models,”
Jonathan Ho, Ajay Jain, and Pieter Abbeel, · 2020
Cited alongside, same era.
“Dual-domain adaptive-scaling non-local network for ct metal artifact reduction,”
Tao Wang, Wenjun Xia, Yongqiang Huang, Huaiqiang Sun, Yan Liu, Hu Chen, Jiliu Zhou, and Yi Zhang, · 2021
Later among the works it cites.
“Dicdnet: Deep interpretable convolutional dictionary network for metal artifact reduction in ct images,”
Hong Wang, Yuexiang Li, Nanjun He, Kai Ma, Deyu Meng, and Yefeng Zheng, · 2021
Later among the works it cites.
“Diffusion models beat gans on image synthesis,”
Prafulla Dhariwal and Alexander Nichol, · 2021
Later among the works it cites.
“Solving inverse problems in medical imaging with score-based generative models,”
Yang Song, Liyue Shen, Lei Xing, and Stefano Ermon, · 2021
Later among the works it cites.
“Repaint: Inpainting using denoising diffusion probabilistic models,”
Andreas Lugmayr, Martin Danelljan, Andres Romero, Fisher Yu, Radu Timofte, and Luc Van Gool, · 2022
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“Fourier features let networks learn high frequency functions in low dimensional domains,”
Matthew Tancik, Pratul Srinivasan, Ben Mildenhall, Sara Fridovich-Keil, Nithin Raghavan, Utkarsh Singhal, Ravi Ramamoorthi, Jonathan Barron, and Ren Ng, · 2020
Cited alongside, same era.
Hyungjin Chung and Jong Chul Ye, · 2022
Closest in time.