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Image reconstruction from computed tomography (CT) measurement is a challenging statistical inverse problem since a high-dimensional conditional distribution needs to be estimated.
Guided image generation with conditional invertible neural networks
Ardizzone, L., Lüth, C., Kruse, J., Rother, C., and Köthe, U · 1907
Earlier work this paper cites.
Three-dimensional reconstruction from radiographs and electron micrographs: Application of convolutions instead of fourier transforms
Ramachandran, G. N. and Lakshminarayanan, A. V · 1971
Earlier work this paper cites.
The fourier reconstruction of a head section
Shepp, L. A. and Logan, B. F · 1974
Earlier work this paper cites.
On the determination of functions from their integral values along certain manifolds
Radon, J · 1986
Earlier work this paper cites.
The mathematics of computerized tomography
Natterer, F · 2001
Earlier work this paper cites.
Image quality assessment: from error visibility to structural similarity
Wang, Z., Bovik, A. C., Sheikh, H. R., and Simoncelli, E. P · 2003
Earlier work this paper cites.
Computed Tomography: From Photon Statistics to Modern Cone-Beam CT
Buzug, T · 2008
Earlier work this paper cites.
The Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI): A completed reference database of lung nodules on CT scans
Armato III, S. G., McLennan, G., Bidaut, L., McNitt-Gray, M. F., Meyer, C. R., Reeves, A. P., Zhao, B., Aberle, D. R., Henschke, C. I., Hoffman, E. A., Kazerooni, E. A., MacMahon, H., van Beek, E. J. R., Yankelevitz, D., Biancardi, A. M., Bland, P. H., Brown, M. S., Engelmann, R. M., Laderach, G. E., Max, D., Pais, R. C., Qing, D. P.-Y., Roberts, R. Y., Smith, A. R., Starkey, A., Batra, P., Caligiuri, P., Farooqi, A., Gladish, G. W., Jude, C. M., Munden, R. F., Petkovska, I., Quint, L. E., Schwartz, L. H., Sundaram, B., Dodd, L. E., Fenimore, C., Gur, D., Petrick, N., Freymann, J., Kirby, J., Hughes, B., Vande Casteele, A., Gupte, S., Sallam, M., Heath, M. D., Kuhn, M. H., Dharaiya, E., Burns, R., Fryd, D. S., Salganicoff, M., Anand, V., Shreter, U., Vastagh, S., Croft, B. Y., and Clarke, L. P · 2011
Earlier work this paper cites.
Adam: A Method for Stochastic Optimization
Kingma, D. P. and Ba, J · 2014
Earlier work this paper cites.
Loss functions for image restoration with neural networks
Zhao, H., Gallo, O., Frosio, I., and Kautz, J · 2016
Earlier work this paper cites.
Solving ill-posed inverse problems using iterative deep neural networks
Adler, J. and Öktem, O · 2017
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Low-dose CT with a residual encoder-decoder convolutional neural network
Chen, H., Zhang, Y., Kalra, M. K., Lin, F., Chen, Y., Liao, P., Zhou, J., and Wang, G · 2017
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The Bayesian Approach to Inverse Problems , pp. 311–428
Dashti, M. and Stuart, A. M · 2017
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Density estimation using real NVP
Dinh, L., Sohl-Dickstein, J., and Bengio, S · 2017
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Deep convolutional neural network for inverse problems in imaging
Jin, K. H., McCann, M. T., Froustey, E., and Unser, M · 2017
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Assessment of structural similarity in CT using filtered backprojection and iterative reconstruction: a phantom study with 3D printed lung vessels
Glow: Generative flow with invertible 1x1 convolutions
Kingma, D. P. and Dhariwal, P · 2018
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fastMRI: An open dataset and benchmarks for accelerated MRI
Zbontar, J., Knoll, F., Sriram, A., Muckley, M. J., Bruno, M., Defazio, A., Parente, M., Geras, K. J., Katsnelson, J., Chandarana, H., Zhang, Z., Drozdzal, M., Romero, A., Rabbat, M., Vincent, P., Pinkerton, J., Wang, D., Yakubova, N., Owens, E., Zitnick, C. L., Recht, M. P., Sodickson, D. K., and Lui, Y. W · 2018
Later among the works it cites.
Solving inverse problems using data-driven models
Arridge, S., Maass, P., Öktem, O., and Schönlieb, C.-B · 2019
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Invertible residual networks
Behrmann, J., Grathwohl, W., Chen, R. T. Q., Duvenaud, D., and Jacobsen, J.-H · 2019
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The LoDoPaB-CT Dataset: A benchmark dataset for low-dose CT reconstruction methods
Leuschner, J., Schmidt, M., Baguer, D. O., and Maaß, P · 2019
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Joemai, R. M. S. and Geleijns, J · 2017
Cited alongside, same era.
Adler, J. and Öktem, O · 2018
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Learned primal-dual reconstruction
Adler, J. and Öktem, O · 2018
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i-RevNet: Deep invertible networks
Jacobsen, J.-H., Smeulders, A., and Oyallon, E · 2018
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Analyzing inverse problems with invertible neural networks
Ardizzone, L., Kruse, J., Rother, C., and Köthe, U
Cited in the paper.
Competitive performance of a modularized deep neural network compared to commercial algorithms for low-dose CT image reconstruction
Shan, H., Padole, A., Homayounieh, F., Kruger, U., Khera, R. D., Nitiwarangkul, C., Kalra, M. K., and Wang, G · 2019
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Learning likelihoods with conditional normalizing flows
Winkler, C., Worrall, D., Hoogeboom, E., and Welling, M · 2019
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Computed tomography reconstruction using deep image prior and learned reconstruction methods
Baguer, D. O., Leuschner, J., and Schmidt, M · 2020
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Radon inversion via deep learning
He, J., Wang, Y., and Ma, J · 2020
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