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

TitleAn Iteration Method for X-Ray CT Reconstruction from Variable-Truncation Projection Data
Authors
KeywordsIn-situ X-ray CT
Noise estimation
Occluded projection data
Sparse representation
Issue Date2019
Citation
Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2019, v. 11603 LNCS, p. 144-155 How to Cite?
AbstractIn 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.
Persistent Identifierhttp://hdl.handle.net/10722/363328
ISSN
2023 SCImago Journal Rankings: 0.606

 

DC FieldValueLanguage
dc.contributor.authorHuo, Limei-
dc.contributor.authorLuo, Shousheng-
dc.contributor.authorDong, Yiqiu-
dc.contributor.authorTai, Xue Cheng-
dc.contributor.authorWang, Yang-
dc.date.accessioned2025-10-10T07:46:04Z-
dc.date.available2025-10-10T07:46:04Z-
dc.date.issued2019-
dc.identifier.citationLecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2019, v. 11603 LNCS, p. 144-155-
dc.identifier.issn0302-9743-
dc.identifier.urihttp://hdl.handle.net/10722/363328-
dc.description.abstractIn 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.-
dc.languageeng-
dc.relation.ispartofLecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics-
dc.subjectIn-situ X-ray CT-
dc.subjectNoise estimation-
dc.subjectOccluded projection data-
dc.subjectSparse representation-
dc.titleAn Iteration Method for X-Ray CT Reconstruction from Variable-Truncation Projection Data-
dc.typeConference_Paper-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1007/978-3-030-22368-7_12-
dc.identifier.scopuseid_2-s2.0-85068447157-
dc.identifier.volume11603 LNCS-
dc.identifier.spage144-
dc.identifier.epage155-
dc.identifier.eissn1611-3349-

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