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Regularization strategies in statistical image reconstruction of low‐dose x‐ray CT : A review
Author(s) -
Zhang Hao,
Wang Jing,
Zeng Dong,
Tao Xi,
Ma Jianhua
Publication year - 2018
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.13123
Subject(s) - regularization (linguistics) , iterative reconstruction , image quality , fidelity , maximum a posteriori estimation , computer science , inverse problem , a priori and a posteriori , artificial intelligence , mathematics , computer vision , image (mathematics) , maximum likelihood , statistics , telecommunications , mathematical analysis , philosophy , epistemology
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 .