An efficient iterative CBCT reconstruction approach using gradient projection sparse reconstruction algorithm

Heui Chang Lee, Bongyong Song, Jin Sung Kim, James J. Jung, Hui Harold Li, Sasa Mutic, Justin C. Park

Research output: Contribution to journalArticlepeer-review

10 Scopus citations


The purpose of this study is to develop a fast and convergence proofed CBCT reconstruction framework based on the compressed sensing theory which not only lowers the imaging dose but also is computationally practicable in the busy clinic. We simplified the original mathematical formulation of gradient projection for sparse reconstruction (GPSR) to minimize the number of forward and backward projections for line search processes at each iteration. GPSR based algorithms generally showed improved image quality over the FDK algorithm especially when only a small number of projection data were available. When there were only 40 projections from 360 degree fan beam geometry, the quality of GPSR based algorithms surpassed FDK algorithm within 10 iterations in terms of the mean squared relative error. Our proposed GPSR algorithm converged as fast as the conventional GPSR with a reasonably low computational complexity. The outcomes demonstrate that the proposed GPSR algorithm is attractive for use in real time applications such as on-line IGRT.

Original languageEnglish
Pages (from-to)87342-87350
Number of pages9
Issue number52
StatePublished - 2016


  • Backtracking line search
  • Compressed sensing
  • Cone-beam computed tomography (CBCT)
  • Gradient projection
  • Low-dose imaging

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