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Interior tomography for the region-of-interest (ROI) imaging has advantages of using a small detector and reducing X-ray radiation dose.
J.-F. Cai, R. H. Chan, and Z. Shen, “A framelet-based image inpainting algorithm,” Applied and Computational Harmonic Analysis , vol. 24, no. 2, pp. 131–149, 2008
2008
Earlier work this paper cites.
H. Yu and G. Wang, “Compressed sensing based interior tomography,” Physics in Medicine and Biology , vol. 54, no. 9, p. 2791, 2009
2009
Earlier work this paper cites.
E. Katsevich, A. Katsevich, and G. Wang, “Stability of the interior problem with polynomial attenuation in the region of interest,” Inverse problems , vol. 28, no. 6, p. 065022, 2012
2012
Earlier work this paper cites.
A. Katsevich and A. Tovbis, “Finite Hilbert transform with incomplete data: null-space and singular values,” Inverse Problems , vol. 28, no. 10, p. 105006, 2012
2012
Earlier work this paper cites.
J. P. Ward, M. Lee, J. C. Ye, and M. Unser, “Interior tomography using 1D generalized total variation – part I: mathematical foundation,” SIAM Journal on Imaging Sciences , vol. 8, no. 1, pp. 226–247, 2015
2015
Cited alongside, same era.
M. Lee, Y. Han, J. P. Ward, M. Unser, and J. C. Ye, “Interior tomography using 1d generalized total variation. part II: Multiscale implementation,” SIAM Journal on Imaging Sciences , vol. 8, no. 4, pp. 2452–2486, 2015
2015
Cited alongside, same era.
O. Ronneberger, P. Fischer, and T. Brox, “U-net: Convolutional networks for biomedical image segmentation,” in International Conference on Medical Image Computing and Computer-Assisted Intervention . Springer, 2015, pp. 234–241
2015
Cited alongside, same era.
2016
Cited alongside, same era.
E. Kang, J. Min, and J. C. Ye, “A deep convolutional neural network using directional wavelets for low-dose x-ray CT reconstruction,” Medical physics , vol. 44, no. 10, 2017
2017
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H. Chen, Y. Zhang, M. K. Kalra, F. Lin, Y. Chen, P. Liao, J. Zhou, and G. Wang, “Low-dose CT with a residual encoder-decoder convolutional neural network,” IEEE transactions on medical imaging , vol. 36, no. 12, pp. 2524–2535, 2017
2017
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K. H. Jin, M. T. McCann, E. Froustey, and M. Unser, “Deep convolutional neural network for inverse problems in imaging,” IEEE Transactions on Image Processing , vol. 26, no. 9, pp. 4509–4522, 2017
2017
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2017
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