Metal Artifact Reduction in Spectral X-ray CT Using Spectral Deep Learning

Matteo Busi*, Christian Kehl, Jeppe R. Frisvad, Ulrik L. Olsen

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

10 Downloads (Pure)

Abstract

Spectral X-ray computed tomography (SCT) is an emerging method for non-destructive imaging of the inner structure of materials. Compared with the conventional X-ray CT, this technique provides spectral photon energy resolution in a finite number of energy channels, adding a new dimension to the reconstructed volumes and images. While this mitigates energy-dependent distortions such as beam hardening, metal artifacts due to photon starvation effects are still present, especially for low-energy channels where the attenuation coefficients are higher. We present a correction method for metal artifact reduction in SCT that is based on spectral deep learning. The correction efficiently reduces streaking artifacts in all the energy channels measured. We show that the additional information in the energy domain provides relevance for restoring the quality of low-energy reconstruction affected by metal artifacts. The correction method is parameter free and only takes around 15 ms per energy channel, satisfying near-real time requirement of industrial scanners.
Original languageEnglish
Article number77
JournalJournal of Imaging
Volume8
Issue number3
Number of pages14
DOIs
Publication statusPublished - 2022

Keywords

  • Spectral convolutional neural networks
  • Spectral X-ray CT
  • Metal Artifact Reduction
  • Computed tomography
  • Non-destructive evaluation
  • Spectral deep learning

Fingerprint

Dive into the research topics of 'Metal Artifact Reduction in Spectral X-ray CT Using Spectral Deep Learning'. Together they form a unique fingerprint.

Cite this