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Computed tomography for region-of-interest (ROI) reconstruction has advantages of reducing X-ray radiation dose and using a small detector.
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2015
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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 8
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2016
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Y. Zou and X. Pan, “Exact image reconstruction on PI-lines from minimum data in helical cone-beam CT,” Physics in Medicine and Biology 49
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Y. Zou and X. Pan, “Image reconstruction on PI-lines by use of filtered backprojection in helical cone-beam CT,” Physics in Medicine and Biology 49
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2018
Closest in time.
J. C. Ye, Y. Han, and E. Cha, “Deep convolutional framelets: A general deep learning framework for inverse problems,” SIAM Journal on Imaging Sciences 11
2018
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Y. Han, J. Gu, and J. C. Ye, “Deep learning interior tomography for region-of-interest reconstruction,” in Proceedings of The Fifth International Conference on Image Formation in X-Ray Computed Tomography (2018)
2018
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