Abstract
In this paper, we investigate the in-situ X-ray CT reconstruction from occluded projection data. For each X-ray beam, we propose a method to determine whether it passes through a measured object by comparing the observed data before and after the measured object is placed. Therefore, we can obtain a prior knowledge of the object, that is some points belonging to the background, from the X-ray beam paths that do not pass through the object. We incorporate this prior knowledge into the sparse representation method for in-situ X-ray CT reconstruction from occluded projection data. In addition, the regularization parameter can be determined easily using the artifact severity estimation on the identified background points. Numerical experiments on simulated data with different noise levels are conducted to verify the effectiveness of the proposed method.
S. Luo was supported by the National Natural Science Foundation of China via Grant A011703. Programs for Science and Technology Development of He’nan Province (192102310181). Y. Dong was supported by the National Natural Science Foundation of China via Grant 11701388. X. C. Tai was supported by the startup grant at Hong Kong Baptist University, grants RG(R)-RC/17-18/02-MATH and FRG2/17-18/033. Y. Wang was supported in part by the Hong Kong Research Grant Council grants 16306415 and 16308518.
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Huo, L., Luo, S., Dong, Y., Tai, XC., Wang, Y. (2019). An Iteration Method for X-Ray CT Reconstruction from Variable-Truncation Projection Data. In: Lellmann, J., Burger, M., Modersitzki, J. (eds) Scale Space and Variational Methods in Computer Vision. SSVM 2019. Lecture Notes in Computer Science(), vol 11603. Springer, Cham. https://doi.org/10.1007/978-3-030-22368-7_12
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