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Applications of nonlocal means algorithm in low‐dose X‐ray CT image processing and reconstruction: A review
Author(s) -
Zhang Hao,
Zeng Dong,
Zhang Hua,
Wang Jing,
Liang Zhengrong,
Ma Jianhua
Publication year - 2017
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1002/mp.12097
Subject(s) - iterative reconstruction , streak , noise (video) , image quality , algorithm , artificial intelligence , computer science , computer vision , image processing , aliasing , projection (relational algebra) , image restoration , quantum noise , filter (signal processing) , image (mathematics) , physics , optics , quantum mechanics , quantum
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 corrupted 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.