Low-rank flat-field correction for artifact reduction in spectral computed tomography

Research output: Contribution to journalJournal articleResearchpeer-review

12 Downloads (Pure)

Abstract

Spectral computed tomography has received considerable interest in recent years since spectral measurements contain much richer information about the object of interest. In spectral computed tomography, we are interested in the energy channel-wise reconstructions of the object. However, such reconstructions suffer from a low signal-to-noise ratio and share the challenges of conventional low-dose computed tomography such as ring artifacts. Ring artifacts arise from errors in the flat fields and can significantly degrade the quality of the reconstruction. We propose an extended flat-field model that exploits high correlation in the spectral flat fields to reduce ring artifacts in channel-wise reconstructions. The extended model relies on the assumption that the spectral flat fields can be well-approximated by a low-rank matrix. Our proposed model works directly on the spectral flat fields and can be combined with any existing reconstruction model, e.g. filtered back projection and iterative methods. The proposed model is validated on a neutron data set. The results show that our method successfully diminishes ring artifacts and improves the quality of the reconstructions. Moreover, the results indicate that our method is robust; it only needs a single spectral flat-field image, whereas existing methods need multiple spectral flat-field images to reach a similar level of ring reduction.
Original languageEnglish
Article number2176000
JournalApplied Mathematics in Science and Engineering
Volume31
Issue number1
Number of pages17
ISSN2769-0911
DOIs
Publication statusPublished - 2023

Keywords

  • Ring artifacts
  • Flat-field correction
  • Low-rank approximation
  • Neutron imaging
  • Spectral computed tomography

Fingerprint

Dive into the research topics of 'Low-rank flat-field correction for artifact reduction in spectral computed tomography'. Together they form a unique fingerprint.

Cite this