Regularization strategies in statistical image reconstruction of low-dose x-ray CT: A review

Hao Zhang, Jing Wang, Dong Zeng, Xi Tao, Jianhua Ma

Research output: Contribution to journalReview articlepeer-review

29 Scopus citations


Statistical image reconstruction (SIR) methods have shown potential to substantially improve the image quality of low-dose x-ray computed tomography (CT) as compared to the conventional filtered back-projection (FBP) method. According to the maximum a posteriori (MAP) estimation, the SIR methods are typically formulated by an objective function consisting of two terms: (a) a data-fidelity term that models imaging geometry and physical detection processes in projection data acquisition, and (b) a regularization term that reflects prior knowledge or expectations of the characteristics of the to-be-reconstructed image. SIR desires accurate system modeling of data acquisition, while the regularization term also has a strong influence on the quality of reconstructed images. A variety of regularization strategies have been proposed for SIR in the past decades, based on different assumptions, models, and prior knowledge. In this paper, we review the conceptual and mathematical bases of these regularization strategies and briefly illustrate their efficacies in SIR of low-dose CT.

Original languageEnglish (US)
Pages (from-to)e886-e907
JournalMedical physics
Issue number10
StatePublished - Oct 2018


  • low dose
  • regularization
  • statistical image reconstruction
  • x-ray CT

ASJC Scopus subject areas

  • Biophysics
  • Radiology Nuclear Medicine and imaging


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