An Iteration Method for X-Ray CT Reconstruction from Variable-Truncation Projection Data

Limei Huo; Shousheng Luo; Yiqiu Dong; Xue Cheng Tai; Yang Wang

doi:10.1007/978-3-030-22368-7_12

An Iteration Method for X-Ray CT Reconstruction from Variable-Truncation Projection Data

Limei Huo, Shousheng Luo^*, Yiqiu Dong, Xue Cheng Tai, Yang Wang

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

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.

Original language	English
Title of host publication	Proceedings of 7th International Conference on Scale Space and Variational Methods in Computer Vision
Editors	Jan Lellmann, Jan Modersitzki, Martin Burger
Publisher	Springer
Publication date	1 Jan 2019
Pages	144-155
ISBN (Print)	9783030223670
DOIs	https://doi.org/10.1007/978-3-030-22368-7_12
Publication status	Published - 1 Jan 2019
Event	7th International Conference on Scale Space and Variational Methods in Computer Vision - Evangelische Tagungsstätte Hofgeismar, Hofgeismar, Germany Duration: 30 Jun 2019 → 4 Jul 2019 Conference number: 7

Conference

Conference	7th International Conference on Scale Space and Variational Methods in Computer Vision
Number	7
Location	Evangelische Tagungsstätte Hofgeismar
Country/Territory	Germany
City	Hofgeismar
Period	30/06/2019 → 04/07/2019

Series	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	11603 LNCS
ISSN	0302-9743

Keywords

In-situ X-ray CT
Noise estimation
Occluded projection data
Sparse representation

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10.1007/978-3-030-22368-7_12

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Huo, L., Luo, S., Dong, Y., Tai, X. C., & Wang, Y. (2019). An Iteration Method for X-Ray CT Reconstruction from Variable-Truncation Projection Data. In J. Lellmann, J. Modersitzki, & M. Burger (Eds.), Proceedings of 7th International Conference on Scale Space and Variational Methods in Computer Vision (pp. 144-155). Springer. https://doi.org/10.1007/978-3-030-22368-7_12

Huo, Limei ; Luo, Shousheng ; Dong, Yiqiu et al. / An Iteration Method for X-Ray CT Reconstruction from Variable-Truncation Projection Data. Proceedings of 7th International Conference on Scale Space and Variational Methods in Computer Vision. editor / Jan Lellmann ; Jan Modersitzki ; Martin Burger. Springer, 2019. pp. 144-155 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 11603 LNCS).

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title = "An Iteration Method for X-Ray CT Reconstruction from Variable-Truncation Projection Data",

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.",

keywords = "In-situ X-ray CT, Noise estimation, Occluded projection data, Sparse representation",

author = "Limei Huo and Shousheng Luo and Yiqiu Dong and Tai, {Xue Cheng} and Yang Wang",

year = "2019",

month = jan,

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doi = "10.1007/978-3-030-22368-7_12",

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series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

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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 J Lellmann, J Modersitzki & M Burger (eds), Proceedings of 7th International Conference on Scale Space and Variational Methods in Computer Vision. Springer, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11603 LNCS, pp. 144-155, 7th International Conference on Scale Space and Variational Methods in Computer Vision, Hofgeismar, Germany, 30/06/2019. https://doi.org/10.1007/978-3-030-22368-7_12

An Iteration Method for X-Ray CT Reconstruction from Variable-Truncation Projection Data. / Huo, Limei; Luo, Shousheng; Dong, Yiqiu et al.
Proceedings of 7th International Conference on Scale Space and Variational Methods in Computer Vision. ed. / Jan Lellmann; Jan Modersitzki; Martin Burger. Springer, 2019. p. 144-155 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 11603 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

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AU - Huo, Limei

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AU - Tai, Xue Cheng

AU - Wang, Yang

N1 - Conference code: 7

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N2 - 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.

AB - 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.

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Huo L, Luo S, Dong Y, Tai XC, Wang Y. An Iteration Method for X-Ray CT Reconstruction from Variable-Truncation Projection Data. In Lellmann J, Modersitzki J, Burger M, editors, Proceedings of 7th International Conference on Scale Space and Variational Methods in Computer Vision. Springer. 2019. p. 144-155. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 11603 LNCS). doi: 10.1007/978-3-030-22368-7_12

An Iteration Method for X-Ray CT Reconstruction from Variable-Truncation Projection Data

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Keywords

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