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 proceedingConference contributionpeer-review

1 Citation (Scopus)

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 languageEnglish
Title of host publicationScale Space and Variational Methods in Computer Vision - 7th International Conference, SSVM 2019, Proceedings
EditorsJan Lellmann, Jan Modersitzki, Martin Burger
PublisherSpringer Verlag
Pages144-155
Number of pages12
ISBN (Print)9783030223670
DOIs
Publication statusPublished - 2019
Event7th International Conference on Scale Space and Variational Methods in Computer Vision, SSVM 2019 - Hofgeismar, Germany
Duration: 30 Jun 20194 Jul 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11603 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference7th International Conference on Scale Space and Variational Methods in Computer Vision, SSVM 2019
Country/TerritoryGermany
CityHofgeismar
Period30/06/194/07/19

Scopus Subject Areas

  • Theoretical Computer Science
  • Computer Science(all)

User-Defined Keywords

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

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