Limited tomography reconstruction via tight frame and simultaneous sinogram extrapolation

Jae Kyu Choi, Bin Dong, Xiaoqun Zhang

Research output: Contribution to journalArticlepeer-review

13 Scopus citations


X-ray computed tomography (CT) is one of widely used diagnostic tools for medical and dental tomographic imaging of the human body. However, the standard filtered back-projection reconstruction method requires the complete knowledge of the projection data. In the case of limited data, the inverse problem of CT becomes more ill-posed, which makes the reconstructed image deteriorated by the artifacts. In this paper, we consider two dimensional CT reconstruction using the projections truncated along the spatial direction in the Radon domain. Over the decades, the numerous results including the sparsity model based approach has enabled the reconstruction of the image inside the region of interest (ROI) from the limited knowledge of the data. However, unlike these existing methods, we try to reconstruct the entire CT image from the limited knowledge of the sinogram via the tight frame regularization and the simultaneous sinogram extrapolation. Our proposed model shows more promising numerical simulation results compared with the existing sparsity model based approach.

Original languageEnglish (US)
Pages (from-to)575-589
Number of pages15
JournalJournal of Computational Mathematics
Issue number6
StatePublished - Nov 1 2016


  • Bregmanized operator splitting algorithm
  • Data driven tight frame
  • Limited tomography
  • Sinogram extrapolation
  • Wavelet frame
  • X-ray computed tomography

ASJC Scopus subject areas

  • Computational Mathematics


Dive into the research topics of 'Limited tomography reconstruction via tight frame and simultaneous sinogram extrapolation'. Together they form a unique fingerprint.

Cite this