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

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedings – Annual report year: 2019Researchpeer-review

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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 languageEnglish
Title of host publicationProceedings of 7th International Conference on Scale Space and Variational Methods in Computer Vision
EditorsJan Lellmann, Jan Modersitzki, Martin Burger
PublisherSpringer
Publication date1 Jan 2019
Pages144-155
ISBN (Print)9783030223670
DOIs
Publication statusPublished - 1 Jan 2019
Event7th International Conference on Scale Space and Variational Methods in Computer Vision, SSVM 2019 - Hofgeismar, Germany
Duration: 30 Jun 20194 Jul 2019

Conference

Conference7th International Conference on Scale Space and Variational Methods in Computer Vision, SSVM 2019
CountryGermany
CityHofgeismar
Period30/06/201904/07/2019
SeriesLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11603 LNCS
ISSN0302-9743
CitationsWeb of Science® Times Cited: No match on DOI

    Research areas

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

ID: 189968426