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001     436634
005     20250716151823.0
024 7 _ |a 10.1107/S1600577520000831
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024 7 _ |a 1600-5775
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024 7 _ |a 10.3204/PUBDB-2020-01013
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100 1 _ |a Yang, Xiaogang
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245 _ _ |a Tomographic reconstruction with a generative adversarial network
260 _ _ |a [S.l.]
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520 _ _ |a This paper presents a deep learning algorithm for tomographic reconstruction(GANrec). The algorithm uses a generative adversarial network (GAN) to solvethe inverse of the Radon transform directly. It works for independent sinogramswithout additional training steps. The GAN has been developed to fit the inputsinogram with the model sinogram generated from the predicted reconstruction.Good quality reconstructions can be obtained during the minimization of thefitting errors. The reconstruction is a self-training procedure based on thephysics model, instead of on training data. The algorithm showed significantimprovements in the reconstruction accuracy, especially for missing-wedgetomography acquired at less than 180 rotational range. It was also validatedby reconstructing a missing-wedge X-ray ptychographic tomography (PXCT)data set of a macroporous zeolite particle, for which only 51 projections over70 could be collected. The GANrec recovered the 3D pore structure withreasonable quality for further analysis. This reconstruction concept can workuniversally for most of the ill-posed inverse problems if the forward model iswell defined, such as phase retrieval of in-line phase-contrast imaging.
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700 1 _ |a Kahnt, Maik
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700 1 _ |a Brückner, Dennis
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700 1 _ |a Schropp, Andreas
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700 1 _ |a Fam, Yakub
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700 1 _ |a Becher, Johannes
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700 1 _ |a Grunwaldt, Jan-Dierk
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700 1 _ |a Sheppard, Thomas L.
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700 1 _ |a Schroer, Christian G.
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773 _ _ |a 10.1107/S1600577520000831
|g Vol. 27, no. 2, p. 486 - 493
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|t Journal of synchrotron radiation
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