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Tensor framelet based iterative image reconstruction algorithm for low-dose multislice helical CT

Author

Listed:
  • Haewon Nam
  • Minghao Guo
  • Hengyong Yu
  • Keumsil Lee
  • Ruijiang Li
  • Bin Han
  • Lei Xing
  • Rena Lee
  • Hao Gao

Abstract

In this study, we investigate the feasibility of improving the imaging quality for low-dose multislice helical computed tomography (CT) via iterative reconstruction with tensor framelet (TF) regularization. TF based algorithm is a high-order generalization of isotropic total variation regularization. It is implemented on a GPU platform for a fast parallel algorithm of X-ray forward band backward projections, with the flying focal spot into account. The solution algorithm for image reconstruction is based on the alternating direction method of multipliers or the so-called split Bregman method. The proposed method is validated using the experimental data from a Siemens SOMATOM Definition 64-slice helical CT scanner, in comparison with FDK, the Katsevich and the total variation (TV) algorithm. To test the algorithm performance with low-dose data, ACR and Rando phantoms were scanned with different dosages and the data was equally undersampled with various factors. The proposed method is robust for the low-dose data with 25% undersampling factor. Quantitative metrics have demonstrated that the proposed algorithm achieves superior results over other existing methods.

Suggested Citation

  • Haewon Nam & Minghao Guo & Hengyong Yu & Keumsil Lee & Ruijiang Li & Bin Han & Lei Xing & Rena Lee & Hao Gao, 2019. "Tensor framelet based iterative image reconstruction algorithm for low-dose multislice helical CT," PLOS ONE, Public Library of Science, vol. 14(1), pages 1-17, January.
    • Handle: RePEc:plo:pone00:0210410
    DOI: 10.1371/journal.pone.0210410
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