A New Iterative Method for CT Reconstruction with Uncertain View Angles

Nicolai André Brogaard Riis*, Yiqiu Dong

*Corresponding author for this work

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Abstract

In this paper, we propose a new iterative algorithm for Computed Tomography (CT) reconstruction when the problem has uncertainty in the view angles. The algorithm models this uncertainty by an additive model-discrepancy term leading to an estimate of the uncertainty in the likelihood function. This means we can combine state-of-the-art regularization priors such as total variation with this likelihood. To achieve a good reconstruction the algorithm alternates between updating the CT image and the uncertainty estimate in the likelihood. In simulated 2D numerical experiments, we show that our method is able to improve the relative reconstruction error and visual quality of the CT image for the uncertain-angle CT problem.

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
Pages156-167
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

Keywords

  • Computed Tomography
  • Model discrepancy
  • Model error
  • Total variation
  • Uncertain view angles
  • Variational methods

Cite this

Riis, N. A. B., & Dong, Y. (2019). A New Iterative Method for CT Reconstruction with Uncertain View Angles. In J. Lellmann, J. Modersitzki, & M. Burger (Eds.), Proceedings of 7th International Conference on Scale Space and Variational Methods in Computer Vision (pp. 156-167). Springer. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol.. 11603 LNCS https://doi.org/10.1007/978-3-030-22368-7_13