Line integral alternating minimization algorithm for dual-energy X-ray CT image reconstruction

Yaqi Chen, Joseph A. O'Sullivan, David G. Politte, Joshua D. Evans, Dong Han, Bruce R. Whiting, Jeffrey F. Williamson

Research output: Contribution to journalArticlepeer-review

13 Scopus citations


We propose a new algorithm, called line integral alternating minimization (LIAM), for dual-energy X-ray CT image reconstruction. Instead of obtaining component images by minimizing the discrepancy between the data and the mean estimates, LIAM allows for a tunable discrepancy between the basis material projections and the basis sinograms. A parameter is introduced that controls the size of this discrepancy, and with this parameter the new algorithm can continuously go from a two-step approach to the joint estimation approach. LIAM alternates between iteratively updating the line integrals of the component images and reconstruction of the component images using an image iterative deblurring algorithm. An edge-preserving penalty function can be incorporated in the iterative deblurring step to decrease the roughness in component images. Images from both simulated and experimentally acquired sinograms from a clinical scanner were reconstructed by LIAM while varying the regularization parameters to identify good choices. The results from the dual-energy alternating minimization algorithm applied to the same data were used for comparison. Using a small fraction of the computation time of dual-energy alternating minimization, LIAM achieves better accuracy of the component images in the presence of Poisson noise for simulated data reconstruction and achieves the same level of accuracy for real data reconstruction.

Original languageEnglish
Article number2490658
Pages (from-to)685-698
Number of pages14
JournalIEEE Transactions on Medical Imaging
Issue number2
StatePublished - Feb 2016


  • Alternating minimization algorithm
  • Dual-energy
  • Iterative deblurring algorithm
  • Line integral
  • X-ray CT


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