Journal of Northeastern University Natural Science ›› 2018, Vol. 39 ›› Issue (7): 964-969.DOI: 10.12068/j.issn.1005-3026.2018.07.011

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Total Variation Regularization CT Iterative Reconstruction Algorithm Based on Augmented Lagrangian Method

XIAO Da-yu, GUO Yang, LI Jian-hua, KANG Yan   

  1. School of Sino-Dutch Biomedical & Information Engineering, Northeastern University, Shenyang 110169, China.
  • Received:2017-03-21 Revised:2017-03-21 Online:2018-07-15 Published:2018-07-11
  • Contact: KANG Yan
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Abstract: A novel algorithm based on augmented Lagrangian method was presented to solve total variation regularization problem (ALMTVR) of the CT iterative reconstruction. The classical algebraic reconstruction technique (ART) was compared with the ALMTVR algorithm, the simulation data and actual data are used in the experiment. The ALMTVR algorithm and the ART algorithm were used to reconstruct the images respectively, and the reconstruction images were compared and analyzed. Results showed that, compared with ART algorithm, the proposed algorithm can significantly improve image quality and reconstruction speed, which indicates the proposed algorithm is effective and has potential applications in the CT imaging system.

Key words: CT iterative reconstruction, augmented Lagrangian method, total variation regularization, simulation data, real projection data

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