Sequentially optimized projections in x-ray imaging

M. Burger, A. Hauptmann, T. Helin, N. Hyvönen*, J. P. Puska

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

1 Citation (Scopus)


This work applies Bayesian experimental design to selecting optimal projection geometries in (discretized) parallel beam x-ray tomography assuming the prior and the additive noise are Gaussian. The introduced greedy exhaustive optimization algorithm proceeds sequentially, with the posterior distribution corresponding to the previous projections serving as the prior for determining the design parameters, i.e. the imaging angle and the lateral position of the source-receiver pair, for the next one. The algorithm allows redefining the region of interest after each projection as well as adapting parameters in the (original) prior to the measured data. Both A and D-optimality are considered, with emphasis on efficient evaluation of the corresponding objective functions. Two-dimensional numerical experiments demonstrate the functionality of the approach.

Original languageEnglish
Article number075006
Number of pages25
JournalInverse Problems
Issue number7
Publication statusPublished - Jul 2021
MoE publication typeA1 Journal article-refereed


  • A-optimality
  • Bayesian experimental design
  • D-optimality
  • optimal projec-tions
  • parallel beam tomography
  • sequential optimization
  • x-ray tomography


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