Undersampled dynamic X-ray tomography with dimension reduction Kalman filter

Janne Hakkarainen, Zenith Purisha, Antti Solonen, Samuli Siltanen

Research output: Contribution to journalArticleScientificpeer-review


In this paper, we propose prior-based dimension reduction Kalman filter for undersampled dynamic X-ray tomography. With this method, the X-ray reconstructions are parameterized by a low-dimensional basis. Thus, the proposed method is a) computationally very light; and b) extremely robust as all the computations can be done explicitly. With real and simulated measurement data, we show that the method provides accurate reconstructions even with very limited number of angular directions.
Original languageEnglish
Pages (from-to)492-501
Number of pages10
Journal IEEE Transactions on Computational Imaging
Issue number3
Publication statusPublished - 2019
MoE publication typeA1 Journal article-refereed


  • covariance matrices
  • dimensionality reduction
  • Kalman filters
  • X-ray tomography
  • image reconstruction
  • time measurement
  • X-ray imaging


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