Applications of nonlocal means algorithm in low-dose X-ray CT image processing and reconstruction: A review

Hao Zhang, Dong Zeng, Hua Zhang, Jing Wang, Zhengrong Liang, Jianhua Ma

Research output: Contribution to journalArticlepeer-review

76 Scopus citations


Low-dose X-ray computed tomography (LDCT) imaging is highly recommended for use in the clinic because of growing concerns over excessive radiation exposure. However, the CT images reconstructed by the conventional filtered back-projection (FBP) method from low-dose acquisitions may be severely degraded with noise and streak artifacts due to excessive X-ray quantum noise, or with view-aliasing artifacts due to insufficient angular sampling. In 2005, the nonlocal means (NLM) algorithm was introduced as a non-iterative edge-preserving filter to denoise natural images cor-rupted by additive Gaussian noise, and showed superior performance. It has since been adapted and applied to many other image types and various inverse problems. This paper specifically reviews the applications of the NLM algorithm in LDCT image processing and reconstruction, and explicitly demonstrates its improving effects on the reconstructed CT image quality from low-dose acquisitions. The effectiveness of these applications on LDCT and their relative performance are described in detail.

Original languageEnglish (US)
Pages (from-to)1168-1185
Number of pages18
JournalMedical physics
Issue number3
StatePublished - Mar 2017


  • X-ray CT denoising
  • dose reduction
  • nonlocal means
  • streak artifacts
  • view-aliasing artifacts

ASJC Scopus subject areas

  • Biophysics
  • Radiology Nuclear Medicine and imaging


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